Year: 2024
Type: article
Abstract: Insulator defect detection is a critical aspect of grid inspection in reality, yet it faces intricate environmental challenges, such as slow detection speed and low accuracy. To address this issue, we propose a YOLOv8-based insulator defect detection algorithm named CDDCR–YOLOv8. This algorithm divides the input insulator images into multiple grid cells, with each grid cell responsible for predicting the presence and positional information of one or more targets. First, we introduce the Coordinate Attention (CA) mechanism module into the backbone network and replace the original C2f module with the enhanced C2f_DCN module. Second, improvements are made to the original upsampling and downsampling layers in the neck network, along with the introduction of the lightweight module RepGhost. Finally, we employ Wise-IoU (WIoU) to replace the original CIoU as the loss function for network regression. Experimental results demonstrate that the improved algorithm achieves an average precision mean (mAP @ 0.5) of 97.5% and 90.6% on the CPLID and IPLID data sets, respectively, with a frame per second (FPS) of 84, achieving comprehensive synchronous improvement. Compared to traditional algorithms, our algorithm exhibits significant performance enhancement.
Authors: Tingyao Jiang, Xuan Hou, Min Wang
Language: English
Topic: Power Line Inspection Robots
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,290
Award: G313450149
OpenAlex ID: W4402601918