Improved RetinaNet Target Detection Method for Power Equipment

doi:10.3969/j.issn.1006-2475.2024.01.008

Abstract

Abstract: Abstract: A RetinaNet-based target detection method for power equipment detection is proposed for the problem of low accuracy of small target recognition in power equipment detection. The anchor box size of original network is optimized by K-means clustering method firstly. Then shallow feature maps with higher resolution are added to feature fusion to solve the problem that the feature maps contain too little information after convolution through multiple layers. Based on this， ECA （Efficient Channel Attention） attention mechanism is introduced to enable the network to locate the effective features of power devices and suppress the useless feature information. The experimental results show that compared with the original method， the average recognition accuracy of the method in the paper is improved by 18.1 percentage points for five types of power equipment: electric towers， pins， construction vehicles， insulators and poles， which indicates that the improved method can significantly improve the detection level of power equipment.

Key words: Key words: object detection, power equipment, cluster analysis, feature extraction, attention mechanism

CLC Number:

TP391

WANG Qiu-yi, ZHOU Hao, ZHENG Ting-ting. Improved RetinaNet Target Detection Method for Power Equipment [J]. Computer and Modernization, 2024, 0(01): 47-52.

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