A defencing algorithm based on deep learning improves the detection accuracy of caged chickens

Cage farming is the mainstream farming mode in China. Accurate individual identification and behavioral detection of caged chickens can provide managers with a better understanding of chicken status. However, for image detection of caged chickens, the cage may affect the accuracy of the detection algorithm. For this reason, CCD (caged chicken defencing), a defencing algorithm based on U-Net and pix2pixHD, was proposed to improve caged chickens' detection accuracy. The proposed defencing algorithm can accurately identify the cage wire mesh and recover the chicken contours completely. In the test set, the detection accuracy of the cage wire mesh was 94.71%, while a structural similarity (SSIM) of 90.04% and a peak signal-to-noise ratio (PSNR) of 25.24 dB were obtained in the image recovery. To verify the practicality of the method proposed in this paper, we analyzed the performance of the object detection algorithm before and after defencing from the perspective of the most basic individual detection in the caged chicken detection task. We validated the defencing algorithm with different YOLOv5 detection algorithms, including YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x. The experimental results showed that the defencing algorithm improved the detection precision of caged chickens by 16.1%, 12.1%, 7.3%, and 5.4%, respectively, compared with before defencing. The recall improvement was 29.1%, 16.4%, 8.5%, and 6.8%. To our knowledge, this is the first time that a deep learning-based defencing algorithm has been applied to caged chickens, and the detection accuracy can be significantly improved. The method proposed in this paper can remove cage wire mesh greatly and provide a technical reference for subsequent poultry researchers.

Automated summary

This paper proposes a contour detection algorithm based on U-Net and pix2pixHD, which can accurately identify the cage wire mesh of caged chickens and recover the complete chicken contours. The algorithm was validated with different YOLOv5 detection algorithms, showing significant improvement in detection precision and recall for caged chickens.