A Lightweight Ground Wire Defect Detection Method Based on LSD and Deep Learning
The detection of defects in ground wires is often slowed down and less accurate due to the large size of UAV aerial images and complex background environment.To address this issue,this paper proposes a lightweight ground wire defect detection method based on LSD and deep learning.First,the LSD algorithm is used to extract linear features from the images.Then,a segmentation baseline is fit by combining it with RANSAC.Based on the segmentation baseline,the region of the ground wire is now clearly segmented,background interferences are eliminated,and image size sent to the detection network reduced.After modifying the YOLOv5 backbone network,the number of parameters is reduced,making it easier to deploy on edge computing equipment.The proposed method reduces the inspected area and suppresses interference.It improves the accuracy from the original 67.9％to 71.3％,at the same time,the detection speed is increased by 12％.It has the advantages of high precision and fast speed,which is suitable for deployment on edge computing equipment.
detection of defects in ground wiresUAV aerial imagesline segment detectline fittingartificial intelligence
万方数据
维普
