SOD-YOLO: A High-Precision Detection of Small Targets on High-Voltage Transmission Lines

Abstract

Wire clamps and vibration-proof hammers are key components of high-voltage transmission lines. The wire clips and vibration-proof hammers detected in Unmanned Aerial Vehicle (UAV) power inspections suffer from small size, scarce edge information, and low recognition accuracy. To address these problems, this paper proposes a small object detection (SOD) model based on the YOLOv8n, called SOD-YOLO. Firstly, an extra small target detection layer was added to YOLOv8, which significantly improves the small target detection accuracy. In addition, in order to enhance the detection speed of the model, the RepVGG/RepConv ShuffleNet (RCS) and a OneShot Aggregation of the RCS (RCSOSA) module were introduced to replace the C2f module in the model backbone and neck shallow networks. Finally, to address the excessive focus on low-quality sample bounding boxes during model training, we introduced Wise-CIoU loss instead of CIoU loss, which improved the detection accuracy of the model. The experimental results indicate that SOD-YOLO achieved a mean average precision of 90.1%, surpassing the YOLOv8n baseline model by 7.5% while maintaining a model parameter count of 3.4 M; the inference speed reached 88.7 frames/s, which meets the requirement of real-time recognition.

OD-YOLO: Robust Small Object Detection Model in Remote Sensing Image with a Novel Multi-Scale Feature Fusion

“…In OIoU, there is another part of the distance loss that more accurately describes the variance in shape and distance in the predicted box and the real box. Here, we use η to represent it, with the specific calculation process shown as Equation (12).…”

“…Tang et al [ 11 ] improved the object detection ability of the YOLO model in complex traffic roads by introducing an RMA module. Wu et al [ 12 ] proposed the SOD-YOLO model to achieve outstanding capacity in detecting mini-goal in the high-voltage transmission lines. Yuan et al [ 13 ] effectively improved the precision in detecting defects of PCB by using the LW-YOLO model.…”