A new anti-vibration hammer rust detection algorithm based on improved YOLOv7

Abstract

Anti-vibration hammer corrosion seriously endangers the safe operation of transmission lines. To better improve the detection effect of anti-vibration hammer corrosion on transmission lines under complex background, in this paper we propose an anti-vibration hammer corrosion detection algorithm based on improved YOLOv7. Firstly, a dataset was established for anti-vibration hammer corrosion detection. After that, a color space transformation was carried out through an HSV color model to highlight the corrosion features. BiFPN multi-scale feature fusion was then introduced to fully utilize the feature maps at different scales, so as to get a weighted feature fusion of weights. Finally, a GSConv module was added to the YOLOv7 network to avoid spatial information loss in the transmission process as far as possible. It can accelerate the inference speed and increase the model accuracy as well. The test results show that the improved algorithm improves the detection accuracy by 1.6% and inference speed by 17% on average compared to the original network.