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1.
Mar Pollut Bull ; 208: 117019, 2024 Sep 25.
Artículo en Inglés | MEDLINE | ID: mdl-39326329

RESUMEN

The unmanned aerial vehicle (UAV) is usually flexible and frequently low-altitude flying without the influence of clouds and severe weather, and it is widely used for port oil spill detection (OSD). However, the background of the port is usually complex, the oil spills in UAV images are usually small and irregular, as well as the oil boundary is fuzzy, which has led to the failure of existing methods in accurately detecting the port oil spill. Here, we propose a scene-class guided dual branch network for port OSD based on UAV images, which can locate the oil spill areas of different sizes and suppress the influence caused by complex backgrounds. Specifically, the dual-branch network consists of semantic segmentation and image classification branches. The image classification branch utilizes the scene-class as the label and further can extract the feature attention, which can guide the semantic segmentation branch to learn the key area features. Second, we propose a multi-scale arbitrary shape convolution module, which can address the challenges caused by fuzzy oil boundaries and irregular small objects. Finally, due to the imbalance between oil spill pixels and other pixels, we design a joint loss to optimize the network. We evaluate our proposed method on a public UAV OSD dataset. The results show that our method is superior to the state-of-the-art method, achieving mIoU of 90.22 %, A of 96.03 %, P of 91.99 %, R of 92.56 %, and F1 of 92.28 %, which represents the feasibility of our method in port OSD and its potential to save a lot of manpower and material resources. The ablation experiment further demonstrates the effectiveness of each designed part.

2.
Animals (Basel) ; 14(6)2024 Mar 14.
Artículo en Inglés | MEDLINE | ID: mdl-38539999

RESUMEN

Animal tracking is crucial for understanding migration, habitat selection, and behavior patterns. However, challenges in video data acquisition and the unpredictability of animal movements have hindered progress in this field. To address these challenges, we present a novel animal tracking method based on correlation filters. Our approach integrates hand-crafted features, deep features, and temporal context information to learn a rich feature representation of the target animal, enabling effective monitoring and updating of its state. Specifically, we extract hand-crafted histogram of oriented gradient features and deep features from different layers of the animal, creating tailored fusion features that encapsulate both appearance and motion characteristics. By analyzing the response map, we select optimal fusion features based on the oscillation degree. When the target animal's state changes significantly, we adaptively update the target model using temporal context information and robust feature data from the current frame. This updated model is then used for re-tracking, leading to improved results compared to recent mainstream algorithms, as demonstrated in extensive experiments conducted on our self-constructed animal datasets. By addressing specific challenges in animal tracking, our method offers a promising approach for more effective and accurate animal behavior research.

3.
Sci Total Environ ; 916: 169873, 2024 Mar 15.
Artículo en Inglés | MEDLINE | ID: mdl-38199362

RESUMEN

The fragile Loess Plateau of China suffers substantial gully erosion. It is imperative to elucidate gully erosion patterns for implementing effective erosion control strategies. However, high spatiotemporal resolution quantification of gully dynamics remains limited across the Loess Plateau landscape. We utilized the small baseline subset interferometric synthetic aperture radar (SBAS InSAR) technique to investigate the phenomenon of gully erosion and deposition on the Dongzhiyuan tableland, which sits within the vast expanse of the Loess Plateau in China, over the period spanning 2020-2022. The tableland edges subsided while gully bottoms uplifted due to sedimentation. Low elevations underwent active deformation. Slope, aspect, and curvature modulated uplift and subsidence patterns by affecting runoff and sediment transport. Gentle downstream slopes displayed enhanced sedimentation. Southern gullies showed pronounced uplift compared to northern gullies. Heavy rainfall triggered extensive erosion followed by rapid uplift, reflecting an adaptive oscillation between erosion and deposition. Basin hydrology correlated with spatial patterns of deformation. Vegetation cover above 60 % of the maximum substantially increased InSAR error. Our study reveals intricate spatiotemporal behaviors of erosion and deposition in loess gullies using time-series InSAR. The findings provide new insights into gully geomorphology and evolution, and our study quantifies gully erosion and deposition patterns at high spatiotemporal resolution, enabling identification of the most vulnerable areas and prioritization of conservation efforts.

4.
Sci Rep ; 13(1): 7178, 2023 May 03.
Artículo en Inglés | MEDLINE | ID: mdl-37137973

RESUMEN

In the field of object detection, feature pyramid network (FPN) can effectively extract multi-scale information. However, the majority of FPN-based methods suffer from a semantic gap between features of various sizes before feature fusion, which can lead to feature maps with significant aliasing. In this paper, we present a novel multi-scale semantic enhancement feature pyramid network (MSE-FPN) which consists of three effective modules: semantic enhancement module, semantic injection module, and gated channel guidance module to alleviate these problems. Specifically, inspired by the strong ability of the self-attention mechanism to model context, we propose a semantic enhancement module to model global context to obtain the global semantic information before feature fusion. Then we propose the semantic injection module to divide and merge global semantic information into feature maps at various scales to narrow the semantic gap between features at different scales and efficiently utilize the semantic information of high-level features. Finally, to mitigate feature aliasing caused by feature fusion, the gated channel guidance module selectively outputs crucial features via a gating unit. By replacing FPN with MSE-FPN in Faster R-CNN, our models achieve 39.4 and 41.2 Average precision (AP) using ResNet50 and ResNet101 as the backbone network respectively. When using ResNet-101-64x4d as the backbone, MSE-FPN achieved up to 43.4 AP. Our results demonstrate that replacing FPN with MSE-FPN significantly enhances the detection performance of state-of-the-art FPN-based detectors.

5.
Animals (Basel) ; 12(9)2022 May 09.
Artículo en Inglés | MEDLINE | ID: mdl-35565649

RESUMEN

Camera trapping and video recording are now ubiquitous in the study of animal ecology. These technologies hold great potential for wildlife tracking, but are limited by current learning approaches, and are hampered by dependence on large samples. Most species of wildlife are rarely captured by camera traps, and thus only a few shot samples are available for processing and subsequent identification. These drawbacks can be overcome in multiobject tracking by combining wildlife detection and tracking with few-shot learning. This work proposes a multiobject-tracking approach based on a tracking-by-detection paradigm for wildlife to improve detection and tracking performance. We used few-shot object detection to localize objects using a camera trap and direct video recordings that could augment the synthetically generated parts of separate images with spatial constraints. In addition, we introduced a trajectory reconstruction module for better association. It could alleviate a few-shot object detector's missed and false detections; in addition, it could optimize the target identification between consecutive frames. Our approach produced a fully automated pipeline for detecting and tracking wildlife from video records. The experimental results aligned with theoretical anticipation according to various evaluation metrics, and revealed the future potential of camera traps to address wildlife detection and tracking in behavior and conservation.

6.
Comput Intell Neurosci ; 2021: 7367870, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-34354745

RESUMEN

The growing interest in deep learning approaches to video surveillance raises concerns about the accuracy and efficiency of neural networks. However, fast and reliable detection of abnormal events is still a challenging work. Here, we introduce a two-stream approach that offers an autoencoder-based structure for fast and efficient detection to facilitate anomaly detection from surveillance video without labeled abnormal events. Furthermore, we present post hoc interpretability of feature map visualization to show the process of feature learning, revealing uncertain and ambiguous decision boundaries in the video sequence. Experimental results on Avenue, UCSD Ped2, and Subway datasets show that our method can detect abnormal events well and explain the internal logic of the model at the object level.


Asunto(s)
Redes Neurales de la Computación , Ríos
7.
Comput Intell Neurosci ; 2019: 3679203, 2019.
Artículo en Inglés | MEDLINE | ID: mdl-31814818

RESUMEN

The performance of convolutional neural network- (CNN-) based object detection has achieved incredible success. Howbeit, existing CNN-based algorithms suffer from a problem that small-scale objects are difficult to detect because it may have lost its response when the feature map has reached a certain depth, and it is common that the scale of objects (such as cars, buses, and pedestrians) contained in traffic images and videos varies greatly. In this paper, we present a 32-layer multibranch convolutional neural network named MBNet for fast detecting objects in traffic scenes. Our model utilizes three detection branches, in which feature maps with a size of 16 × 16, 32 × 32, and 64 × 64 are used, respectively, to optimize the detection for large-, medium-, and small-scale objects. By means of a multitask loss function, our model can be trained end-to-end. The experimental results show that our model achieves state-of-the-art performance in terms of precision and recall rate, and the detection speed (up to 33 fps) is fast, which can meet the real-time requirements of industry.


Asunto(s)
Vehículos a Motor , Redes Neurales de la Computación , Reconocimiento de Normas Patrones Automatizadas/métodos , Ciclismo , Ciudades , Humanos , Luz , Factores de Tiempo
8.
Comput Intell Neurosci ; 2017: 5169675, 2017.
Artículo en Inglés | MEDLINE | ID: mdl-28706534

RESUMEN

With the popular use of geotagging images, more and more research efforts have been placed on geographical scene classification. In geographical scene classification, valid spatial feature selection can significantly boost the final performance. Bag of visual words (BoVW) can do well in selecting feature in geographical scene classification; nevertheless, it works effectively only if the provided feature extractor is well-matched. In this paper, we use convolutional neural networks (CNNs) for optimizing proposed feature extractor, so that it can learn more suitable visual vocabularies from the geotagging images. Our approach achieves better performance than BoVW as a tool for geographical scene classification, respectively, in three datasets which contain a variety of scene categories.


Asunto(s)
Geografía , Redes Neurales de la Computación , Reconocimiento de Normas Patrones Automatizadas/métodos
9.
Comput Intell Neurosci ; 2015: 489793, 2015.
Artículo en Inglés | MEDLINE | ID: mdl-26457077

RESUMEN

Context-aware user interface plays an important role in many human-computer Interaction tasks of location based services. Although spatial models for context-aware systems have been studied extensively, how to locate specific spatial information for users is still not well resolved, which is important in the mobile environment where location based services users are impeded by device limitations. Better context-aware human-computer interaction models of mobile location based services are needed not just to predict performance outcomes, such as whether people will be able to find the information needed to complete a human-computer interaction task, but to understand human processes that interact in spatial query, which will in turn inform the detailed design of better user interfaces in mobile location based services. In this study, a context-aware adaptive model for mobile location based services interface is proposed, which contains three major sections: purpose, adjustment, and adaptation. Based on this model we try to describe the process of user operation and interface adaptation clearly through the dynamic interaction between users and the interface. Then we show how the model applies users' demands in a complicated environment and suggested the feasibility by the experimental results.


Asunto(s)
Concienciación , Teléfono Celular , Sistemas de Información Geográfica , Navegación Espacial , Interfaz Usuario-Computador , Humanos
10.
Acta Crystallogr C ; 61(Pt 9): o531-2, 2005 Sep.
Artículo en Inglés | MEDLINE | ID: mdl-16143772

RESUMEN

In the crystal structure of (E)-8-(3-chlorostyryl)-1,3,7-trimethylxanthine (CSC) [systematic name: (E)-8-(3-chlorostyryl)-1,3,7-trimethyl-3,7-dihydro-1H-purine-2,6-dione], C16H15ClN4O2, the xanthine ring and the lateral styryl chain are coplanar. The crystal packing involves mainly parallel stacking of these planar molecules. The electrostatic potential calculated on the crystal structure conformation confirms the pharmacophore elements associated with MAO-B inhibition.


Asunto(s)
Antagonistas del Receptor de Adenosina A2 , Inhibidores de la Monoaminooxidasa/farmacología , Xantinas/farmacología , Modelos Moleculares , Conformación Molecular , Electricidad Estática , Xantinas/química
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