Improved Lightweight YOLOv5 Algorithm for Detecting Grid Inspection Object
Year: 2024
Type: conference-paper
Abstract: In the field of unmanned aerial vehicle-based transmission line inspection, the neural network models currently employed suffer from large model sizes and high computational requirements, resulting in a trade-off between detection speed and accuracy. We propose an improved lightweight YOLOv5 object detection algorithm based on channel pruning to address this issue. The approach incorporates the SimAM attention mechanism into the backbone network to enhance feature extraction capability without increasing computational complexity, thereby improving detection accuracy. Traditional convolutional modules in the neck network are replaced with GSconv modules to reduce convolutional computation and enhance detection speed. The Wise-IoU loss is utilized as the localization loss for the detection model, speeding up model convergence. Finally, a model compression technique is implemented to eliminate unnecessary channels within the network, resulting in a decrease in model size and a boost in detection speed. Experimental results on a constructed grid inspection dataset demonstrate that the improved model achieves a 72.1% reduction in model size, a 62.5% reduction in GFLOPs, a 5.9 FPS improvement, and a 0.3% mAP increase compared to the original YOLOv5. These findings underscore the advantages of the improved lightweight YOLOv5 object detection algorithm, offering a blend of heightened accuracy, streamlined size, and swift detection capabilities.
Authors: Huaqi Liu, Wenbin You, Yonghong Ding
Institution: North University of China
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4402597216