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Detection of Self-explosive Insulators in Aerial Images Based on Improved YOLO v4
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Abstract
In view of the low detection efficiency of traditional aerial image-based self-explosive insulators and the need to manually extract the features of self-explosive insulators, a self-explosive insulator detection algorithm based on the improved YOLO v4 is proposed: We use the hybrid data augmentation method to increase the number of defective samples of self-explosive insulators and increase the diversity of self-explosive insulators to improve the detection accuracy of the algorithm. In addition, the channel attention mechanism is combined with the YOLO v4 algorithm to improve the feature extraction capability of algorithms to self-explosion insulators. The proposed algorithm is verified using the expanded self-explosive insulator dataset and the traffic signs dataset TT100K. The experimental results show that the proposed algorithm has a mAP of 92.0 % on the self-explosive insulator dataset and has an F1-score of 78.70% on TT100K dataset, which has positive significance for the research on self-explosive insulator detection.
Title: Detection of Self-explosive Insulators in Aerial Images Based on Improved YOLO v4
Description:
Abstract
In view of the low detection efficiency of traditional aerial image-based self-explosive insulators and the need to manually extract the features of self-explosive insulators, a self-explosive insulator detection algorithm based on the improved YOLO v4 is proposed: We use the hybrid data augmentation method to increase the number of defective samples of self-explosive insulators and increase the diversity of self-explosive insulators to improve the detection accuracy of the algorithm.
In addition, the channel attention mechanism is combined with the YOLO v4 algorithm to improve the feature extraction capability of algorithms to self-explosion insulators.
The proposed algorithm is verified using the expanded self-explosive insulator dataset and the traffic signs dataset TT100K.
The experimental results show that the proposed algorithm has a mAP of 92.
0 % on the self-explosive insulator dataset and has an F1-score of 78.
70% on TT100K dataset, which has positive significance for the research on self-explosive insulator detection.
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