MCP: Multi-Chicken Pose Estimation Based on Transfer Learning.

Poultry managers can better understand the state of poultry through poultry behavior analysis. As one of the key steps in behavior analysis, the accurate estimation of poultry posture is the focus of this research. This study mainly analyzes a top-down pose estimation method of multiple chickens. Therefore, we propose the "multi-chicken pose" (MCP), a pose estimation system for multiple chickens through deep learning. Firstly, we find the position of each chicken from the image via the chicken detector; then, an estimate of the pose of each chicken is made using a pose estimation network, which is based on transfer learning. On this basis, the pixel error (PE), root mean square error (RMSE), and image quantity distribution of key points are analyzed according to the improved chicken keypoint similarity (CKS). The experimental results show that the algorithm scores in different evaluation metrics are a mean average precision (mAP) of 0.652, a mean average recall (mAR) of 0.742, a percentage of correct keypoints (PCKs) of 0.789, and an RMSE of 17.30 pixels. To the best of our knowledge, this is the first time that transfer learning has been used for the pose estimation of multiple chickens as objects. The method can provide a new path for future poultry behavior analysis.

Preparation and Anti-Icing Properties of Zirconia Superhydrophobic Coating.

Zirconia (ZrO2) is a ceramic material with high-temperature resistance and good insulating properties. Herein, for the first time, the surface of ZrO2 was modified with docosanoic acid (DCA) to improve its self-cleaning and hydrophobic properties. This surface modification transformed the surface of ZrO2 from hydrophilic to superhydrophobic. A two-step spraying method was used to prepare the superhydrophobic surface of ZrO2 by sequentially applying a primer and a topcoat. The primer was a solution configured using an epoxy resin as the adhesive and polyamide as the curing agent, while the topcoat was a modified ZrO2 solution. The superhydrophobic surface of ZrO2 exhibited a contact angle of 154° and a sliding angle of 4°. Scanning electron microscopy, X-ray diffraction, energy-dispersive X-ray spectroscopy, thermogravimetric analysis, and other analytical techniques were used to characterize the prepared zirconia particles and their surfaces. Moreover, results from surface self-cleaning and droplet freezing tests showed that DCA-modified ZrO2 can be well combined, and its coatings show good self-cleaning and anti-icing properties on TA2 bases.

Wettability Study of an Acidified Nano-TiO2 Superhydrophobic Surface.

The operation of aerospace equipment is often affected by icing and frosting. In order to reduce the loss caused by icing in the industrial field, it is an effective method to prepare superhydrophobic coatings by modifying nanoparticles with low surface energy materials. In order to explore a method of preparing a superhydrophobic surface that can be popularized, a two-step spraying method was employed to create a superhydrophobic coating. The surface was characterized by Fourier transform infrared spectroscopy (FTIR) and field emission scanning electron microscopy (SEM). The optimal preparation process was obtained by analyzing the surface contact angle data. The results showed that stearic acid was grafted onto the surface of TiO2 by esterification reaction. The existence of long methyl and methylene hydrophobic groups in the tail of the stearic acid molecule made the modified TiO2 hydrophobic. It is verified that water molecules have strong adsorption on the surface of unmodified TiO2. Stearic acid molecules can reduce the interfacial energy in the system.

Preparation and properties of oxidized multi-walled carbon nanotube superhydrophobic composites modified by bio-fatty acids.

In this paper, a preparation method of superhydrophobic composites of oxidized multi-walled carbon nanotubes modified by stearic acid (SA) is proposed. Hydroxylated multi-walled carbon nanotubes (HMWCNTs) were obtained by oxidizing multi-walled carbon nanotubes with potassium dichromate to give them hydroxyl groups on the surface. Subsequently, the carboxyl group in the SA molecule was esterified with the hydroxyl group on the HMWCNTs. SA molecules were grafted onto the surface of multi-walled carbon nanotubes. SA modified oxidized multi-walled carbon nanotubes (SMWCNT) superhydrophobic composites were obtained. The results show that the water contact angle (WCA) of superhydrophobic composites can reach up to 174°. At the same time, the modified nanocomposites have good anti-icing and corrosion resistance. After low temperature delayed freezing test, the freezing extension time of the nanocomposite film is 30 times that of the smooth surface. Under strong acid and alkali conditions, the superhydrophobic nanocomposites still maintain good superhydrophobicity. The nanocomposites may have potential applications in the preparation of large-scale superhydrophobic coatings.

Super-resolution fusion optimization for poultry detection: a multi-object chicken detection method.

Accurate poultry detection is crucial for studying poultry behavior using computer vision and video surveillance. However, in free-range farming environments, detecting chickens can often be challenging due to their small size and mutual occlusion. The current detection algorithms exhibit a low level of accuracy, with a high probability of false and missed detections. To address this, we proposed a multi-object chicken detection method named Super-resolution Chicken Detection, which utilizes super-resolution fusion optimization. The algorithm employs the residual-residual dense block to extract image features and used a generative adversarial network to compensate for the loss of details during deep convolution, producing high-resolution images for detection. The proposed algorithm was validated with the B1 data set and the MC1 multi-object data set, demonstrating that the reconstructed images possessed richer pixel features compared to original images, specifically it improved detection accuracy and reduced the number of missed detections. The structural similarity of the reconstructed images was 99.9%, and the peak signal-to-noise ratio was above 30. The algorithm improved the Average Precision50:95 of all You Only Look Once Version X (YOLOX) models, with the largest improvement for the B1 data set with YOLOX-Large (+6.3%) and for the MC1 data set with YOLOX-Small (+4.1%). This was the first time a super-resolution reconstruction technique was applied to multi-object poultry detection. Our method will provide a fresh approach for future poultry researchers to improve the accuracy of object detection using computer vision and video surveillance.

In free-range farming environments, accurately detecting individual chickens has been a persistent challenge for researchers. Due to mutual occlusion and limitations of camera capturing distance, existing detection algorithms have had low detection accuracy, leading to a high probability of false and missed detections. To address this problem, a multi-object chicken detection method named Super-resolution Chicken Detection (SRCD) was developed. The proposed algorithm utilized super-resolution reconstruction and You Only Look Once Version X object detection networks to achieve accurate chicken detection. Through extensive experimentation with two data sets, SRCD demonstrated its superiority in detecting chickens, reducing missed detections likely related to occlusion and the chickens' distance to the camera. Additionally, the SRCD algorithm enriched the pixel features of the chickens with minimal changes to the original images, resulting in a reconstructed image with high similarity to the original image. As a result, this method provided a practical solution for more accurate detection in small free-range farming environments, improving poultry production efficiency.

Study on Poultry Pose Estimation Based on Multi-Parts Detection.

Poultry pose estimation is a prerequisite for evaluating abnormal behavior and disease prediction in poultry. Accurate pose-estimation enables poultry producers to better manage their poultry. Because chickens are group-fed, how to achieve automatic poultry pose recognition has become a problematic point for accurate monitoring in large-scale farms. To this end, based on computer vision technology, this paper uses a deep neural network (DNN) technique to estimate the posture of a single broiler chicken. This method compared the pose detection results with the Single Shot MultiBox Detector (SSD) algorithm, You Only Look Once (YOLOV3) algorithm, RetinaNet algorithm, and Faster_R-CNN algorithm. Preliminary tests show that the method proposed in this paper achieves a 0.0128 standard deviation of precision and 0.9218 ± 0.0048 of confidence (95%) and a 0.0266 standard deviation of recall and 0.8996 ± 0.0099 of confidence (95%). By successfully estimating the pose of broiler chickens, it is possible to facilitate the detection of abnormal behavior of poultry. Furthermore, the method can be further improved to increase the overall success rate of verification.

Experimental study of methane hydrate generation characteristics in the presence of GO and Re-GO.

The industrial application of hydrate technology is greatly hindered by its slow generation rate, low gas storage rate, harsh generation conditions, and poor environmental friendliness of traditional additives. In this paper, the kinetic and thermodynamic promotion effects of graphene oxide (GO) and recovered graphene oxide (Re-GO) on methane hydrate in different systems were studied by the constant volume methods. The promotion mechanism was analyzed from the micro perspectives of molecular physical properties, interfacial reaction, and nucleation sites. It is found that GO has an excellent kinetic and thermodynamic promotion effect on CH4 hydrate generation. After the recovery process, the thermodynamic effect of Re-GO is basically unchanged, and the kinetic promotion effect is slightly reduced. Furthermore, it is verified that the GO material itself does not have a memory effect in hydrate formation. The results show that GO is an excellent accelerator of CH4 hydrate formation with high recovery value, which provides essential data and an experimental basis for the research and application of graphene oxide and hydrate technology in energy storage and cold storage.

Design and Implementation of Poultry Farming Information Management System Based on Cloud Database.

Aiming at breaking down the bottleneck problems of different scale of poultry farms, the low profitability of poultry farming, and backward information management in China, a safe and efficient information management system for poultry farming was designed. This system consists of (1) a management system application layer, (2) a data service layer, and (3) an information sensing layer. The information sensing layer obtains and uploads production and farming information through the wireless sensor network built in the poultry house. The use of a cloud database as an information storage carrier in the data service layer eliminates the complex status of deploying local server clusters, and it improves the flexibility and scalability of the system. The management system application layer contains many sub-function modules including poultry disease detection functions to realize the visual management of farming information and health farming; each module operates independently and cooperates with each other to form a set of information management system for poultry farming with wide functional coverage, high service efficiency, safety, and convenience. The system prototype has been tested for the performance of wireless sensor network and cloud database, and the results show that the prototype is capable of acquiring and managing poultry farming information.