Design of x-ray broiler leg disease detection system based on pyside6

With the excessive selection for body weight (BW) and breast muscle weight (BMW) in broiler chickens, the incidence of leg diseases has gradually increased, potentially leading to severe mortality, decreased productivity, and restricted growth. To address this issue, the yolov8 algorithm has been introduced, which demonstrates significant improvements in both speed and accuracy compared to other algorithms. However, due to the high degree of integration of the yolov8 algorithm, training and inference need to be conducted through command-line interfaces, posing significant challenges for developers. To address this challenge, this study designs and develops a graphical user interface (GUI) for yolov8, aiming to facilitate developers in completing yolov8 inference tasks more conveniently and thereby improving development efficiency.