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With the rapid development of China's economy, the whole society's demand for electricity is becoming more and more extensive. Insulator is the guarantee of smooth operation of transmission line, and is also one of the equipment which is prone to failure. How to detect insulator damage with high precision and high efficiency and repair it is one of the key factors of unimpeded power. This paper proposes a method for detection of insulator damage based on improved YOLOv4-tiny network. This method is mainly aimed at improving the main feature extraction module and feature fusion module of traditional YOLOv4-tiny network. In order to solve the problem of missing detection of small targets, adaptive attention mechanism is introduced to improve the module of feature extraction to improve the accuracy of detection. In addition, in order to further improve the detection accuracy of insulator damage and balance the detection time, a multi-attention CSAR model is proposed to improve the performance of the feature fusion module. Finally, images collected from different weather conditions are used as test sets to verify the effect of the improved model proposed in this paper through experiments. According to the experimental results, the detection accuracy of the proposed method can reach 98%, and the detection time is controlled within 10ms, which meets the basic requirements of detection of insulator damage.
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