Improved Yolov7-tiny Algorithm for Steel Surface Defect Detection

被引：9

作者：

Qi, Xiangming ^{[1
]}

Dong, Xu ^{[1
]}

机构：

[1] School of Software, Liaoning University of Technology, Liaoning, Huludao

来源：

Computer Engineering and Applications | 2023年 / 59卷 / 12期

关键词：

BiFPN; CARAFE; defect detection; MHSA; SiLU; SPD; steel surface; Yolov7-tiny;

D O I：

10.3778/j.issn.1002-8331.2302-0191

中图分类号：

学科分类号：

摘要：

In order to improve the efficiency of small target detection of steel surface defects, an improved Yolov7-tiny steel surface defect detection algorithm is proposed. The activation function of the feature extraction network is changed to SiLU to improve the feature extraction capability. The tensor splicing operation of the feature fusion network is combined with the weighted bidirectional feature pyramid BiFPN, and the nearest interpolation of the upper sampling part is replaced with the lightweight operator CARAFE to improve the feature fusion ability. Finally, the multi-head self-attention mechanism MHSA and SPD convolution building blocks are introduced at the output end to improve the detection performance of the output end for small targets of steel surface defects. The ablation and contrast experiments are carried out on the NEU-DET dataset. Compared with the original Yolov7-tiny algorithm, the improved algorithm has increased the mAP by 11.7 percentage points, the precision by 3.3 percentage points, and the FPS value reaches 192. The results show that the improved algorithm can effectively improve the detection efficiency of small targets of steel surface defects. Comparative experiments on the VOC2012 dataset show that the improved algorithm is universal. © 2023 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.

引用

页码：176 / 183

页数：7

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