Detection of Self-explosive Insulators in Aerial Images Based on Improved YOLO v4

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 augmentatio...

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Veröffentlicht in:Journal of physics. Conference series Jg. 2320; H. 1; S. 12025 - 12037
Hauptverfasser: Yi, Li, Jinguo, Li, Yongjie, Zhai, Ping, Meng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Bristol IOP Publishing 01.08.2022
Schlagworte:
Algorithms
Datasets
Explosives detection
Feature extraction
Insulators
Physics
Traffic signs
ISSN:1742-6588, 1742-6596
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Zusammenfassung: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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ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2320/1/012025