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Simple Summary: In the process of large-scale cage chicken breeding, using inspection robots instead of manual detection can solve the problems of large workload and low detection efficiency. However, poor driving stability of an inspection robot will lead to low inspection accuracy. In order to ensure high efficiency, accuracy, and stability of detection, this study takes the stability of visual navigation for inspection robots as the optimization goal, and designs an efficient and accurate road extraction algorithm and a navigation line fitting algorithm with better robustness. The experimental results show that the algorithm proposed in this study can improve the stability of detection, achieve a faster running speed, and achieve a better detection effect and a higher detection efficiency while keeping the detection effect unchanged. The navigation algorithm can accelerate the automation process of large-scale cage chicken breeding and promote the realization of fast and accurate monitoring. The speed and accuracy of navigation road extraction and driving stability affect the inspection accuracy of cage chicken coop inspection robots. In this paper, a new grayscale factor (4B-3R-2G) was proposed to achieve fast and accurate road extraction, and a navigation line fitting algorithm based on the road boundary features was proposed to improve the stability of the algorithm. The proposed grayscale factor achieved 92.918% segmentation accuracy, and the speed was six times faster than the deep learning model. The experimental results showed that at the speed of 0.348 m/s, the maximum deviation of the visual navigation was 4 cm, the average deviation was 1.561 cm, the maximum acceleration was 1.122 m/s2, and the average acceleration was 0.292 m/s2, with the detection number and accuracy increased by 21.125% and 1.228%, respectively. Compared with inertial navigation, visual navigation can significantly improve the navigation accuracy and stability of the inspection robot and lead to better inspection effects. The visual navigation system proposed in this paper has better driving stability, higher inspection efficiency, better inspection effect, and lower operating costs, which is of great significance to promote the automation process of large-scale cage chicken breeding and realize rapid and accurate monitoring. [ABSTRACT FROM AUTHOR]

Simple Summary: This study introduces a novel system called MCP for estimating the poses of multiple chickens using advanced deep learning techniques. Through automatically detecting and analyzing the postures of chickens, the MCP system helps to assess their behavior and welfare more effectively. The developed system uses a method known as transfer learning, which improves its accuracy and efficiency. The test results show that our system performs well, making it a valuable tool for poultry managers and researchers aiming to enhance animal welfare through technology. This work paves the way for future advancements in animal behavior analysis and could be adapted to other species as well. 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 [ABSTRACT FROM AUTHOR]