tibia length measurement, broiler production, digital radiography, deep learning, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

With the continuous improvement of broiler production performance, the frequent occurrence of leg problems has caused serious economic losses in many factories. In order to more efficiently detect and prevent broiler leg diseases, we propose an auxiliary detection system for broiler leg diseases based on deep learning. The system is divided into two parts. First, a digital radiography (DR) image of a broiler is taken through industrial computed tomography (CT), and then the improved deep-learning network Tibia-YOLO is used to detect the tibia; the detected results are then extracted and measured. Our improved Tibia-YOLO network uses the Content-Aware ReAssembly of Features (CARAFE) upsampling operator to avoid checkerboard artifacts and increase the generalization capabilities. Efficient multi-scale attention (EMA) and parallel network attention (ParNet) were added to the Tibia dataset at multiple scales (COCO2016), and there were improvements when testing on the three VOC2012 datasets. The mean average precision of tibia detection reached 90.8%, and the root mean square error (RMSE) for the tibia length was 3.37 mm.