Detection of Leg Diseases in Broiler Chickens Based on Improved YOLOv8 X-Ray Images

With the excessive selection for body weight (BW) and breast muscle weight (BMW) in broiler chickens, the incidence of leg diseases has gradually increased, which may lead to severe mortality, decreased productivity, and growth restrictions. Traditional methods for detecting leg diseases heavily rely on the interpretation of X-ray images by professionals and scoring methods for chicken gait. However, X-ray images of broiler chicken legs suffer from low background contrast and small, blurry lesion areas, posing significant challenges for traditional target detection methods. This paper proposes an improved algorithm based on the latest YOLOv8 for detecting leg diseases in X-ray images of chicken legs. In the feature extraction phase, Partial Convolution (PConv) is introduced to the C2f module, effectively reducing computational complexity while more accurately extracting spatial features. By incorporating Channel Prior Convolutional Attention (CPCA) into the network backbone, dynamic allocation of attention weights in both channel and spatial dimensions is achieved, preventing the loss of feature details caused by convolution iterations and enhancing the representation capability of small object features. The feature fusion stage introduces a novel Gather-Distribute mechanism (GD), effectively improving the inter-layer information exchange. Additionally, a Partial Convolution-based Shared Weight Detection Head (SharedPConv head) is introduced in the network head, making the model more lightweight and effectively alleviating the overfitting issue. Experimental results demonstrate that the improved method achieves a 7.2% increase in average precision, with a speed of 66.8fps, meeting real-time requirements and performing the detection task more accurately.