Gel immersion endoscopic mucosal resection for small gastric neoplastic lesions: A pilot study.

Gastric endoscopic mucosal resection is challenging due to the slippery mucosa, abundant blood vessels, and the presence of mucus. We developed gel immersion endoscopy to secure the visual field, even in a blood-filled gastrointestinal lumen in 2016. Clear gel with appropriate viscosity, instead of water, can prevent rapid mixture with blood and facilitate identification of the culprit vessel. We further optimized the gel for endoscopic treatment, and the resultant product, Viscoclear (Otsuka Pharmaceutical Factory) was first released in Japan in 2020. The viscosity of this gel has been optimized to maximize endoscopic visibility without compromising the ease of its irrigation. The aim of this study is to clarify the effectiveness of gel immersion endoscopic mucosal resection for small-sized early gastric neoplasms. Seven lesions in seven patients were treated by gel immersion endoscopic mucosal resection. The size of all lesions was under 10 mm. The median procedure time was 4.5 min. Intraoperative bleeding occurred in four of seven lesions immediately after snare resection and was easily controlled by endoscopic hemostatic forceps during the gel immersion endoscopy. The R0 resection rate was 100%. In conclusion, gel immersion endoscopic mucosal resection may be a straightforward, rapid, and safe technique for resecting superficial gastric neoplasms <10 mm in diameter.

Highly energy-efficient degradation of simulated dye wastewater by array type underwater bubble discharge plasma: Key factors identification and degradation pathways.

The extensive use of dyes presents a significant safety concern for the reuse of water resources, highlighting the critical need for a rapid and efficient degradation method for dye mixtures in wastewater. This study introduces a degradation system based on an array type underwater bubble discharge plasma specifically designed to treat a dye mixture wastewater containing methylene blue (MB) and methyl violet 2B (MV2B). Analysis of the optical and electrical characteristics reveals that the device experiences minimal temperature increase, with the highest intensity of the band associated with N2 in the emission spectra, and discharges occurring predominantly during the rising and falling edges of a pulse cycle. Experimental results demonstrate that the most effective degradation efficiencies (MB = 94.83%, MV2B = 93.48%) are achieved at an initial dye concentration of 50 mg/L, a power supply frequency of 6 kHz, an air flow rate of 2.5 SLM and an initial electrical conductivity of 50 µS/cm. The degradation of dyes is primarily attributed to the demethylation process of O3(aq) and other species. Toxicity analysis indicates that the plasma degradation process significantly reduces the toxicity of the intermediate products of dyes. This study not only presents a novel approach for treating high concentration mixed dye wastewater, but also provides valuable insights for future research in this area.

Dual design strategy for carboxymethyl cellulose-polyaniline composite hydrogels as super-sensitive amphibious sensors.

Conductive hydrogels as ideal candidate materials for flexible sensors have exhibited many promising applications. However, complex application environments, such as low temperatures or underwater conditions, have introduced new requirements for hydrogel sensors. Herein, a high-performance conductive hydrogel based on carboxymethyl cellulose-polyaniline (CMC-PANI) submicron spheres, poly (vinyl alcohol) (PVA) and phytic acid (PA) was designed and fabricated via a dual design strategy. CMC-PANI particles were introduced to not only empower the good electromechanical performance to the hydrogels, but also enhance the mechanical properties. The obtained hydrogel exhibited good mechanical property, anti-freezing, anti-swellable behavior and recyclable performance. Resistive-type strain sensors assembled by the prepared hydrogels exhibited high pressure sensitivity (34.17×10-2 kPa-1) and fast response time (100 ms), which can clearly detect the pulse beats. Moreover, the hydrogel sensors can achieve long-term stability, high sensitivity and fatigue resistance as an underwater sensor. Based on these favorable performances, the conductive polymer hydrogels may open up an enticing avenue for functional soft materials in health diagnostic and electronic components.

Variable Structure Controller for Energy Savings in an Underwater Sensor Platform.

This paper introduces a new variable structure controller designed for depth control of an autonomous underwater sensor platform equipped with a variable buoyancy module. To that end, the prototype linear model is presented, and a finite element-based method is used to estimate one of its parameters, the hull deformation due to pressure. To manage potential internal disturbances like hull deformation or external disturbances like weight changes, a disturbance observer is developed. An analysis of the observer steady-state estimation error in relation to input disturbances and system parameter uncertainties is developed. The locations of the observer poles according to its parameters are also identified. The variable structure controller is developed, keeping energy savings in mind. The proposed controller engages when system dynamics are unfavorable, causing the vehicle to deviate from the desired reference, and disengages when dynamics are favorable, guiding the vehicle toward the target reference. A detailed analysis determines the necessary switching control actions to ensure the system reaches the desired reference. Finally, simulations are run to compare the proposed controller's performance with that of PID-based controllers recently developed in the literature, assessing dynamic response and energy consumption under various operating conditions. Both the VBM- and propeller-actuated vehicles were evaluated. The results demonstrate that the proposed controller achieves an average energy consumption reduction of 22% compared to the next most efficient PID-based controller for the VBM-actuated vehicle, though with some impact on control performance.

Underwater Image Enhancement Based on Luminance Reconstruction by Multi-Resolution Fusion of RGB Channels.

Underwater image enhancement technology is crucial for the human exploration and exploitation of marine resources. The visibility of underwater images is affected by visible light attenuation. This paper proposes an image reconstruction method based on the decomposition-fusion of multi-channel luminance data to enhance the visibility of underwater images. The proposed method is a single-image approach to cope with the condition that underwater paired images are difficult to obtain. The original image is first divided into its three RGB channels. To reduce artifacts and inconsistencies in the fused images, a multi-resolution fusion process based on the Laplace-Gaussian pyramid guided by a weight map is employed. Image saliency analysis and mask sharpening methods are also introduced to color-correct the fused images. The results indicate that the method presented in this paper effectively enhances the visibility of dark regions in the original image and globally improves its color, contrast, and sharpness compared to current state-of-the-art methods. Our method can enhance underwater images in engineering practice, laying the foundation for in-depth research on underwater images.

Computer-Simulated Virtual Image Datasets to Train Machine Learning Models for Non-Invasive Fish Detection in Recirculating Aquaculture.

Artificial Intelligence (AI) and Machine Learning (ML) can assist producers to better manage recirculating aquaculture systems (RASs). ML is a data-intensive process, and model performance primarily depends on the quality of training data. Relatively higher fish density and water turbidity in intensive RAS culture produce major challenges in acquiring high-quality underwater image data. Additionally, the manual image annotation involved in model training can be subjective, time-consuming, and labor-intensive. Therefore, the presented study aimed to simulate fish schooling behavior for RAS conditions and investigate the feasibility of using computer-simulated virtual images to train a robust fish detection model. Additionally, to expedite the model training and automate the virtual image annotation, a process flow was developed. The 'virtual model' performances were compared with models trained on real-world images and combinations of real and virtual images. The results of the study indicate that the virtual model trained solely with computer-simulated images could not perform satisfactorily (mAP = 62.8%, F1 score = 0.61) to detect fish in a real RAS environment; however, replacing a small number of the virtual images with real images in the training dataset significantly improved the model's performance. The M6 mixed model trained with 630 virtual and 70 real images (virtual-to-real image ratio: 90:10) achieved mAP and F1 scores of 91.8% and 0.87, respectively. Furthermore, the training time cost for the M6 model was seven times shorter than that for the 'real model'. Overall, the virtual simulation approach exhibited great promise in rapidly training a reliable fish detection model for RAS operations.

Lightweight deep learning model for underwater waste segmentation based on sonar images.

In recent years, the rapid accumulation of marine waste not only endangers the ecological environment but also causes seawater pollution. Traditional manual salvage methods often have low efficiency and pose safety risks to human operators, making automatic underwater waste recycling a mainstream approach. In this paper, we propose a lightweight multi-scale cross-level network for underwater waste segmentation based on sonar images that provides pixel-level location information and waste categories for autonomous underwater robots. In particular, we introduce hybrid perception and multi-scale attention modules to capture multi-scale contextual features and enhance high-level critical information, respectively. At the same time, we use sampling attention modules and cross-level interaction modules to achieve feature down-sampling and fuse detailed features and semantic features, respectively. Relevant experimental results indicate that our method outperforms other semantic segmentation models and achieves 74.66 % mIoU with only 0.68 M parameters. In particular, compared with the representative PIDNet Small model based on the convolutional neural network architecture, our method can improve the mIoU metric by 1.15 percentage points and can reduce model parameters by approximately 91 %. Compared with the representative SeaFormer T model based on the transformer architecture, our approach can improve the mIoU metric by 2.07 percentage points and can reduce model parameters by approximately 59 %. Our approach maintains a satisfactory balance between model parameters and segmentation performance. Our solution provides new insights into intelligent underwater waste recycling, which helps in promoting sustainable marine development.

Tannic Acid-Enabled Antioxidant and Stretchable MXene/Silk Strain Sensors for Diving Training Healthcare.

MXene-based conductive hydrogels hold significant promise as epidermal sensors, yet their susceptibility to oxidation represents a formidable limitation. This study addresses this challenge by incorporating MXene into a tannic acid (TA) cross-linked silk fibroin matrix. The resulting conductive hydrogel (denoted as e-dive) exhibits favorable characteristics such as adjustable mechanical properties, self-healing capabilities (both mechanically and electrically), and strong underwater adhesion. The existence of a percolation network of MXene within the nanocomposites guarantees good electrical conductivity. Importantly, the surface interaction of MXene nanosheets with the hydrophobic moiety from TA substantially reduced moisture and oxygen interactions with MXene, thereby effectively mitigating MXene oxidation within hydrogel matrices. This preservation of the electrical characteristics ensures prolonged functional stability. Furthermore, the e-dive demonstrates inherent antibacterial properties, making it suitable for use in underwater environments where bacterial contamination is a concern. The utilization of this advanced e-dive system extends to the correction of diving postures and the facilitation of underwater healthcare and security alerts. Our study presents a robust methodology for enhancing the stability of MXene-based conductive hydrogel electronics, thereby expanding their scope of potential applications.

Thioctic Acid-Based Solvent-Free and Recoverable Adhesive for Dry/Wet Environments.

Metal adhesive synthesis typically involves heating and solvents, and the resultant adhesives lack degradability and suffer from recycling and sustainable problems. Herein, we developed a solvent-free and chemically degradable biobased adhesive (p(Elp-TA)+PVP) from thioctic acid (TA), its derivative (Elp), and polyvinylpyrrolidone (PVP). Through a rapid acid-triggered cationic ring-opening polymerization of dithiolane at ambient conditions, p(Elp-TA)+PVP adhesive could build up a strong lap shear strength of 1123 kPa in air and an underwater lap shear strength of 534 kPa to the copper plate. Molecular dynamics simulations show that compared to p(Elp-TA), the presence of an appropriate amount of PVP can significantly enhance the binding energy of the adhesive molecules to the metal substrate, and the rapid adhesion of p(Elp-TA)+PVP molecules to metal substrates was achieved through a synergistically dynamic adaptive network enhanced by hydrogen bonding, reversible dynamic bonding, and metal coordination bonding at 40 ps. More importantly, the applied p(Elp-TA)+PVP adhesive could be easily degraded and reverted to its small-molecular-weight lipoic acid species. Upon exposure to dithiothreitol, a green reducing agent, the average molecular weight of the adhesive could quickly decrease from 1603 kDa to 274 Da. This green adhesive constructed by a simple method provides a promising general strategy for developing a controlled degradable and recoverable adhesive from natural resources.

PVA-based Hydrogel Materials for Underwater Energy Storage and Underwater Sensing.

As human exploration of marine continues to expand, the demand for underwater devices is also increasing. The unique properties of hydrogel materials make them well-suited for underwater applications. We propose a multi-functional polyvinyl alcohol (PVA) - NaCl @ Polyaniline (PANI) (PNP) hydrogel, which is characterized by easy fabrication, integrated structure, and flexibility, and can be directly applied in the fields of underwater energy storage and underwater sensing. Solid-state supercapacitors fabricated by the PNP hydrogel, due to integrated and all-solid-state design, can be charged and discharged underwater without encapsulation. What's more, the PNP supercapacitor can maintain a capacitance retention rate of over 90% after 5,000 cycles in simulated seawater, eliminating concerns about the hydrogel's dehydration when used underwater. The PNP hydrogel with an integrated three-layer structure can also be applied to the capacitive pressure sensors, which can also be directly used in underwater environments without the need for encapsulation, significantly reducing the structural complexity and preparation steps of the device. Finally, we demonstrate a "supercapacitor module"with a voltage window greater than 1.6 V created by directly connecting multiple PNP supercapacitors in series, as well as an underwater intelligent glove, providing new solutions for underwater energy storage and underwater wearable sensing applications.

Ionic Hydrogel-Based Moisture Electric Generators for Underwater Electronics.

Ubiquitous moisture is of particular interest for sustainable power generation and self-powered electronics. However, current moisture electric generators (MEGs) can only harvest moisture energy in the air, which tremendously limits the energy harvesting efficiency and practical application scenarios. Herein, the operationality of MEG from air to underwater environment, through a sandwiched engineered-hydrogel device with an additional waterproof breathable membrane layer allowing water vapor exchange while preventing liquid water penetration, is expanded. Underwater environment, the device can spontaneously deliver a voltage of 0.55 V and a current density of 130 µA cm-2 due to the efficient ion separation assisted by negative ions confinement in hydrogel networks. The output can be maintained even under harsh underwater environment with 10% salt concentration, 1 m s-1 disturbing flow, as well as >40 kPa hydraulic pressure. The engineered hydrogel used for MEG also exhibits excellent self-healing ability, flexibility, and biocompatibility. As the first demonstration of practical applications in self-powered underwater electronics, the MEG device is successfully powering a wireless emitter for remote communication in water. This new type of MEG offers an innovative route for harvesting moisture energy underwater and holds promise in the creation of a new range of innovative electronic devices for marine Internet-of-Things.

On the Development of an Acoustic Image Dataset for Unexploded Ordnance Classification Using Front-Looking Sonar and Transfer Learning Methods.

This research aimed to develop a dataset of acoustic images recorded by a forward-looking sonar mounted on an underwater vehicle, enabling the classification of unexploded ordnances (UXOs) and objects other than unexploded ordnance (nonUXOs). The dataset was obtained using digital twin simulations performed in the Gazebo environment utilizing plugins developed within the DAVE project. It consists of 69,444 sample images of 512 × 399 resolution organized in two classes annotated as UXO and nonUXO. The obtained dataset was then evaluated by state-of-the-art image classification methods using off-the-shelf models and transfer learning techniques. The research included VGG16, ResNet34, ResNet50, ViT, RegNet, and Swin Transformer. Its goal was to define a base rate for the development of other specialized machine learning models. Neural network experiments comprised two stages-pre-training of only the final layers and pre-training of the entire network. The experiments revealed that to obtain high accuracy, it is required to pre-train the entire network, under which condition, all the models achieved comparable performance, reaching 98% balanced accuracy. Surprisingly, the highest accuracy was obtained by the VGG model.

An Underwater Crack Detection System Combining New Underwater Image-Processing Technology and an Improved YOLOv9 Network.

Underwater cracks are difficult to detect and observe, posing a major challenge to crack detection. Currently, deep learning-based underwater crack detection methods rely heavily on a large number of crack images that are difficult to collect due to their complex and hazardous underwater environments. This study proposes a new underwater image-processing method that combines a novel white balance method and bilateral filtering denoising method to transform underwater crack images into high-quality above-water images with original crack features. Crack detection is then performed based on an improved YOLOv9-OREPA model. Through experiments, it is found that the new image-processing method proposed in this study significantly improves the evaluation indicators of new images, compared with other methods. The improved YOLOv9-OREPA also exhibits a significantly improved performance. The experimental results demonstrate that the method proposed in this study is a new approach suitable for detecting underwater cracks in dams and achieves the goal of transforming underwater images into above-water images.

Underwater Acoustic Orthogonal Frequency-Division Multiplexing Communication Using Deep Neural Network-Based Receiver: River Trial Results.

In this article, a deep neural network (DNN)-based underwater acoustic (UA) communication receiver is proposed. Conventional orthogonal frequency-division multiplexing (OFDM) receivers perform channel estimation using linear interpolation. However, due to the significant delay spread in multipath UA channels, the frequency response often exhibits strong non-linearity between pilot subcarriers. Since the channel delay profile is generally unknown, this non-linearity cannot be modeled precisely. A neural network (NN)-based receiver effectively tackles this challenge by learning and compensating for the non-linearity through NN training. The performance of the DNN-based UA communication receiver was tested recently in river trials in Western Australia. The results obtained from the trials prove that the DNN-based receiver performs better than the conventional least-squares (LS) estimator-based receiver. This paper suggests that UA communication using DNN receivers holds great potential for revolutionizing underwater communication systems, enabling higher data rates, improved reliability, and enhanced adaptability to changing underwater conditions.

RSE-YOLOv8: An Algorithm for Underwater Biological Target Detection.

Underwater target detection is of great significance in underwater ecological assessment and resource development. To better protect the environment and optimize the development of underwater resources, we propose a new underwater target detection model with several innovations based on the YOLOv8 framework. Firstly, the SAConv convolutional operation is introduced to redesign C2f, the core module of YOLOv8, to enhance the network's feature extraction capability for targets of different scales. Secondly, we propose the RFESEConv convolution module instead of the conventional convolution operation in neural networks to cope with the degradation of image channel information in underwater images caused by light refraction and reflection. Finally, we propose an ESPPF module to further enhance the model's multi-scale feature extraction efficiency. Simultaneously, the overall parameters of the model are reduced. Compared to the baseline model, the proposed one demonstrates superior advantages when deployed on underwater devices with limited computational resources. The experimental results show that we have achieved significant detection accuracy on the underwater dataset, with an mAP@50 of 78% and an mAP@50:95 of 43.4%. Both indicators are 2.1% higher compared to the baseline models. Additionally, the proposed model demonstrates superior performance on other datasets, showcasing its strong generalization capability and robustness. This research provides new ideas and methods for underwater target detection and holds important application value.

A Dynamic Trust evaluation and update model using advance decision tree for underwater Wireless Sensor Networks.

Underwater wireless sensor networks (UWSNs) are an emerging research area that is rapidly gaining popularity. However, it has several challenges, including security, node mobility, limited bandwidth, and high error rates. Traditional trust models fail to adapt to the dynamic underwater environment. Thus, to address these issues, we propose a dynamic trust evaluation and update model using a modified decision tree algorithm. Unlike baseline methods, which often rely on static and generalized trust evaluation approaches, our model introduces several innovations tailored specifically for UWSNs. These include energy-aware decision-making, real-time adaptation to environmental changes, and the integration of multiple underwater-specific factors such as water currents and acoustic signal properties. Our model enhances trust accuracy, reduces energy consumption, and lowers data overhead, achieving a 96% accuracy rate with a 2% false positive rate. Additionally, it outperforms baseline models by improving energy efficiency by 50 mW and reducing response time to 20 ms per packet. These innovations demonstrate the proposed model's effectiveness in addressing the unique challenges of UWSNs, ensuring both security and operational efficiency goals. The proposed model effectively enhances the trust evaluation process in UWSNs, providing both security and operational benefits. These key findings validate the potential of integrating modified decision tree algorithms to improve the performance and sustainability of UWSNs.

Arc bubble edge detection method based on deep transfer learning in underwater wet welding.

The stability of arc bubble is a crucial indicator of underwater wet welding process. However, limited research exists on detecting arc bubble edges in such environments, and traditional algorithms often produce blurry and discontinuous results. To address these challenges, we propose a novel arc bubble edge detection method based on deep transfer learning for processing underwater wet welding images. The proposed method integrates two training stages: pre-training and fine-tuning. In the pre-training stage, a large source domain dataset is used to train VGG16 as a feature extractor. In the fine-tuning stage, we introduce the Attention-Scale-Semantics (ASS) model, which consists of a Convolutional Block Attention Module (CBAM), a Scale Fusion Module (SCM) and a Semantic Fusion Module (SEM). The ASS model is further trained on a small target domain dataset specific to underwater wet welding to fine-tune the model parameters. The CBAM can adaptively weight the feature maps, focusing on more crucial features to better capture edge information. The SCM training method maximizes feature utilization and simplifies training by combining multi-scale features. Additionally, the skip structure of SEM effectively mitigates semantic loss in the high-level network, enhancing the accuracy of edge detection. On the BSDS500 dataset and a self-constructed underwater wet welding dataset, the ASS model was evaluated against conventional edge detection models-Richer Convolutional Features (RCF), Fully Convolutional Network (FCN), and UNet-as well as state-of-the-art models LDC and TEED. In terms of Mean Absolute Error (MAE), accuracy, and other evaluation metrics, the ASS model consistently outperforms these models, demonstrating edge detection capabilities that are both effective and stable in detecting arc bubble edges in underwater wet welding images.

Highly Stretchable, Transparent, Solvent-Resistant Multifunctional Ionogel with Underwater Self-Healing and Adhesion for Wearable Strain Sensors and Barrier-Free Information Transfer.

Ionogels with excellent deformability, high ionic conductivity, and a sensitive stimulus response have been widely used and rapidly developed in flexible wearable systems. However, previously reported ionogels are mainly limited to atmospheric environments applications and have difficulty meeting the requirements of solvent-resistant, self-healing, and adhesion properties in underwater environments. Herein, a multifunctional ionogel capable of underwater applications is prepared by one-step photoinitiated polymerization of a fluorine-containing monomer (2,2,3,4,4,4-hexafluorobutyl acrylate, HFBA) and acrylic acid (AA) in a hydrophobic ionic liquid ([EMIM][TFSI]). The dynamic physical interactions of hydrogen bonds and ionic dipoles endow the ionogel with remarkable transparency, tunable mechanical properties, and underwater self-healing properties. Moreover, the fluoropolymer matrix offers high resistance to water and various solvents and exhibits strong underwater adhesion on different substrates. Thus, the sensor based on the ionogel exhibits excellent sensing properties, including high sensitivity, fast response, and superior durability. In particular, the ionogel can be used as a wearable underwater sensor to perform barrier-free information transfer. This study provides a design idea for the development of underwater flexible strain sensors.

MOF-Decorated Poly(tetrafluoroethylene) Membranes with Underwater Superoleophobicity for Extracting Osmotic Energy from Oily Wastewater Effluents.

Industrial processes generate huge volumes of oily saline wastewater. Instead of being sent to the drainage system immediately, extracting osmotic energy from these effluents represents a promising means to reuse these wastes and contributes to mitigate the ever-growing energy crisis. Herein, an MOF-decorated PTFE membrane is engineered to extract osmotic energy from oily wastewaters. Copper hydroxide nanowires (CHNs) are intertwined with polystyrenesulfonate sodium (PSS), deposited onto a poly(tetrafluoroethylene) (PTFE) membrane, and thereafter used as metal precursors to in situ generate HKUST-1 doped with negative charges. The resulting HKUST-1PSS@PTFE hybrid membrane possesses abundant angstrom-scale channels capable of transporting cations efficiently and features a hierarchically structured surface with underwater superoleophobicity. The energy conversion performance of the HKUST-1PSS3.5@PTFE membrane can reach an output power density of 6.21 W m-2 at a 50-fold NaCl gradient, which is superior to those of pristine PTFE membranes. Once exposed to oily saline wastewater, the HKUST-1PSS@PTFE membrane can exhibit an excellent oil-repellent ability, thus contributing to sustain its osmotic energy harvesting. This work may promote the development of antifouling osmotic energy harvesters with a long working life and pave the way to fully exploit oily wastewater effluents as valuable energy sources.

UICE-MIRNet guided image enhancement for underwater object detection.

Underwater object detection is a crucial aspect of monitoring the aquaculture resources to preserve the marine ecosystem. In most cases, Low-light and scattered lighting conditions create challenges for computer vision-based underwater object detection. To address these issues, low-colorfulness and low-light image enhancement techniques are explored. This work proposes an underwater image enhancement technique called Underwater Image Colorfulness Enhancement MIRNet (UICE-MIRNet) to increase the visibility of small, multiple, dense objects followed by underwater object detection using YOLOv4. UICE-MIRNet is a specialized version of classical MIRNet, which handles random increments of brightness features to address the visibility problem. The proposed UICE-MIRNET restrict brightness and also works on the improvement of the colourfulness of underwater images. UICE-MIRNet consists of an Underwater Image-Colorfulness Enhancement Block (UI-CEB). This block enables the extraction of low-colourful areas from underwater images and performs colour correction without affecting contextual information. The primary characteristics of UICE-MIRNet are the extraction of multiple features using a convolutional stream, feature fusion to facilitate the flow of information, preservation of contextual information by discarding irrelevant features and increasing colourfulness through proper feature selection. Enhanced images are then trained using the YOLOv4 object detection model. The performance of the proposed UICE-MIRNet method is quantitatively evaluated using standard metrics such as UIQM, UCIQE, entropy, and PSNR. The proposed work is compared with many existing image enhancement and restoration techniques. Also, the performance of object detection is assessed using precision, recall, and mAP. Extensive experiments are conducted on two standard datasets, Brackish and Trash-ICRA19, to demonstrate the performance of the proposed work compared to existing methods. The results show that the proposed model outperforms many state-of-the-art techniques.