An enhancement algorithm for head characteristics of caged chickens detection based on cyclic consistent migration neural network

• DOI: 10.1016/j.psj.2024.103663
• Publication Date: 2024-03-15T16:20:41Z
• Authors: Zhenwei Yu; Liqing Wan; Khurram Yousaf; Hai Lin; Ji Zhang; Hongchao Jiao; Geqi Yan; Zhanhua Song; Fuyang Tian
• Keywords: Similarity (geometry); Enclosure; SIGNAL (programming language)

The enclosed multistory poultry housing is a type of enclosure widely used in industrial caged chicken breeding. Accurate identification and detection the comb eyes chickens farms using this can enhance managers' understanding health chickens. However, accuracy image will be affected by enclosure's entrance, which reduce precision. Therefore, paper proposes cage-gate removal algorithm based on big data deep learning Cyclic Consistent Migration Neural Network (CCMNN). method achieves automatic elimination restoration some key information through CCMNN network. Structural Similarity Index Measure (SSIM) between recovered original images test set 91.14%. Peak signal-to-noise ratio (PSNR) 25.34dB. To verify practicability proposed method, performance target analyzed both before after applying network detecting combs Different YOLOv8 algorithms, including YOLOv8s, YOLOv8n, YOLOv8m, YOLOv8x, were to paper. experimental results demonstrate that compared without processing, precision improved 11, 11.3, 12.8, 10.2%. Similarly, eye for 2.4, 10.2, 6.8, 9%. more complete outline obtained enhanced. These advancements offer valuable insights future researchers aiming deploy enhanced equipment, thereby contributing accurate assessment production farm conditions.