Angle steel tower bolt defect detection based on YOLO-V3

Abstract

The bolts in the angle steel tower are seriously affected by corrosion and loss. This paper proposes a novel detection system based on YOLO-V3 to avoid the danger of traditional manual detection method for the bolt fault detection of the angle steel tower. A multi-scale convolution module is used to replace the ordinary convolution of original YOLO-V3 so as to obtain the spatial characteristics information of different scales in the image, and enhance the detection accuracy. The experimental results show that mAP of the proposed YOLO-SKIP network is 0.91. Our YOLO-SKIP model has achieved the best detection performance on the defective angle steel tower bolt data.