AD-YOLOv5: An object detection approach for key parts of sika deer based on deep learning

• Published in: Computers and electronics in agriculture 2024-02, Vol.217, p.108610, Article 108610
• Main Authors: Xiong, Haitao, Xiao, Ying, Zhao, Haiping, Xuan, Kui, Zhao, Yao, Li, Juan
• Format: Article
• Language: English
• Subjects: Attention mechanism; Bi-directional feature pyramid network; Deep learning; Key parts; Object detection; Sika deer
• ISSN: 0168-1699; 1872-7107
• DOI: 10.1016/j.compag.2024.108610

•This paper establishes an image dataset of sika deer obtained from different perspectives.•This paper proposes an AD-YOLOv5 model to detect key parts of the sika deer.•This paper improves the loss function of the model to enhance both the training and inference ability of the model.•This paper proposes an adaptive channel attention mechanism for better extracting shallow features of the key parts of sika deer.•By altering the feature fusion network and combining it with the channel attention mechanism, the feature information is balanced across different scales, which enhances the accuracy of detection. Accurate and rapid fixation of key parts of sika deer is essential for the mechanized and intelligent collection of both deer blood and velvet antler. To solve the low detection accuracy problem of key parts of deer caused by the diversity of deer farm breeding environment and the rapid movement of deer, this paper proposes an AD-YOLOv5 algorithm for detecting key parts of sika deer based on YOLOv5s. Specifically, a new weighted bidirectional feature pyramid network S-BiFPN is first proposed, which both simplifies the structure and effectively performs skip feature fusion to better capture object information at different scales, so that the adaptive feature fusion capability of the model is optimized. Second, this paper proposes to add a SENet module after the C3 module of the 9th Layer in the backbone part of the network, so that the network can adaptively readjust the channel weights of feature maps to enhance the important features for key parts of sika deer and improve the expression ability of the model for key parts. Furthermore, to make the model more stable in the training process, a novel bounding box regression loss function SIoU is led into, which can better learn the position and size of the bounding box by fusing the orientation information between the ground truth box and the predicted box. The effectiveness of the proposed algorithm is demonstrated through ablation experiments. Experimental results show that the mAP@0.5 of the proposed algorithm reaches 97.30 % for the detection of key parts of sika deer, which is 12.85 %, 9.65 %, 9.18 %, 7.13 %, 3.59 %, 4.6 %, 0.90 % and 0.10 % higher than SSD, EfficientDet, YOLOv3, YOLOv4, Faster R-CNN, YOLOv5s, YOLOv7 and YOLOv8s, respectively. This research provides a new idea for detecting key parts of animals in complex environments and lays a theoretical foundation for the development of intelligent f