Improved Aircraft Flared Tube Defect Detection Algorithm of YOLOv4 Network Structure

Abstract

Abstract When detecting aircraft flared tube defect with the YOLOv4 network structure, tiny object defects will be missed, and resulting in a high missed detection rate and low mAP (mean Average Precision) value. This paper proposes an improved aircraft flared tube defect detection algorithm of YOLOv4 network structure. Firstly, in order to improve the feature extraction capability of the YOLOv4 network for tiny defects, a convolution operation is added to the SPP (Spatial Pyramid Pooling) and PANet (Path Aggregation Network) structure. Secondly, the representation of the feature pyramid is enhanced utilizing the improved PANet. Thirdly, the decoupled head is utilized to improve the model performance. Finally, we construct the aircraft flared tube dataset by labeling the defect samples, and experiment with the improved YOLOv4 network. Experimental results show that the mAP value of defect detection task is increased from 91.26% to 95.31%, average detection time increased from 346.17ms to 278.61ms.