A Method for Detecting the Death State of Caged Broilers Based on Improved Yolov5

In the unmanned breeding mode of broilers, the automatic identification of the death state of broilers in a complex cage environment can effectively improve the picking efficiency and reduce its negative impact. An improved Yolov5 object detection method is proposed in this study to rapidly and accurately detect the presence of dead broilers in caged structures. The Yolov5 model within the CSPDarknet53 framework is constructed, and the Yolov5 model is experimentally pruned to reduce the number of convolution kernels. The Ghost structure and the Involution structure are combined with the Yolov5 backbone network to effectively reduce the number of parameters and computation with a negligible loss of accuracy, making it easier to deploy applications. The results show that with the decrease of the accuracy of 0.1%, The number of parameters is reduced from 7.05M to 0.31M, and the amount of calculation is reduced from 16.3 to 0.3. It can be seen that the improved Yolov5 network is effective and suitable for high-precision real-time detection and recognition of the death state of broilers in a caged structure.