基于YOLO v8-ST的叠层笼养肉鸡死鸡识别方法OA

A detection model based on the improved YOLO v8 was constructed to address the issue of automatic dead chicken detection in large-scale commercial broiler farms.Based on a self-built dataset,the YOLO v8-ST model was proposed.In the C2f module,the shift-wise convolution was introduced to simulate large kernel receptive fields through small kernel convolutions,thereby enhancing the feature extraction capability in complex scenarios.Meanwhile,the Power Transform(PT)function was introduced to optimize the overlap calculation in the alignment metric,improving the learning ability of high-quality prediction boxes.Experimental results showed that the precision,recall,and average precision of the YOLO v8-ST model reached 94.7%,92.8%,and 97.6%,respectively,which were 4.2,4.1,4.0 percentage points higher than the baseline.To address the issue of high model complexity,the LAMP pruning method was introduced for lightweight optimization.When the global pruning rate was 33%,the average precision of the model was decreased by only 2.0 percentage points,while the floating-point operation count was reduced by approximately 33.0%,and the number of parameters and model size(9.1 MB)were reduced by 60.0%and 57.0%,respectively.The results of multi-camera experiments indicated that the single-camera model performed optimally on homologous data;multi-camera data fusion could significantly improve detection performance;and low-illumination cameras had the potential to replace expensive industrial cameras in low-illumination environments.The methods and conclusions proposed for dead chicken detection can provide theoretical references for the automatic inspection of dead chickens in broiler farms.