Surface defect detection algorithm based on improved YOLOv4

被引：4
|
作者：
Li B. [1 ]
Wang C. [1 ]
Ding X. [1 ]
Ju H. [1 ]
Guo Z. [1 ]
Li Z. [1 ]
机构：
[1] Fundamentals Department, Air Force Engineering University, Xi’an
来源：
Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics | 2023年 / 49卷 / 03期
关键词：
aeroengine; focal loss; small target detection; surface defect detection; YOLOv4;
D O I：
10.13700/j.bh.1001-5965.2021.0301
中图分类号：
学科分类号：
摘要：
In order to enhance the accuracy and speed of surface defect detection of aeroengine components, an improved YOLOv4 algorithm is proposed for intelligent detection. Firstly, shallow features and deep features were integrated into the path aggregation network (PANet) to improve the feature detection scale, and the bottom-up path augmentation structure was removed to increase the accuracy of small target detection and the overall detection speed. Then, according to the numbers of various defects, the balance parameter of the focal loss was optimized, and a weight factor was added to adjust the loss weight of various defects. The improved focal loss was used to replace the cross-entropy loss function in the classification error, thus reducing the impact of imbalanced samples and hard and easy samples on the detection accuracy. The experimental results show that the mean average precision (mAP) of the improved YOLOv4 on the test set is 90.10%, which is 2.17% higher than that of the traditional YOLOv4, and the detection speed is 24.82 fps, which is increased by 1.58 fps. The detection accuracy is also higher than other algorithms including single shot multibox detector (SSD), EfficientDet, YOLOv3 and YOLOv4-Tiny. © 2023 Beijing University of Aeronautics and Astronautics (BUAA). All rights reserved.
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收藏
页码：710 / 717
页数：7
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