Open AccessDetection of leg diseases in broiler chickens based on improved YOLOv8 X-ray images
Author(s) -
Xin Zhang,
Changxi Chen,
Renwen Zhu,
Weigang Zheng
Publication year - 2024
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2024.3382193
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
In X-ray images of chicken leg diseases, the low background contrast, small and blurry lesion areas pose significant challenges to traditional object detection, particularly for accurate detection of small targets. This paper proposes an improved algorithm based on the latest YOLOv8 for the detection of chicken leg diseases in X-ray images. 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.