Steel Defect Detection Based on Yolov5
Authors: 
Wanyu Deng, Wen MaAuthors Info & Claims
Wanyu Deng, Wen MaAuthors Info & ClaimsPages 880 - 888
Abstract
With the innovation of the industrial revolution, the role of industrial production in people's life is becoming more and more important, and as one of the indispensable basic industries in the industry - steel, its demand is also growing rapidly. The good quality of steel determines the quality and life cycle of industrial products, but the current artificial detection is also certain limitations. With the rapid development of deep learning, it is of great significance to combine defect detection with deep learning [1-4]. Therefore, an improved steel defect detection algorithm based on Yolov5 [5] was proposed. In the improved algorithm, attention mechanism [6] was added to the convolutional network [7] module to strengthen the extraction of network features. The lightweight CARAFE [8] up-sampling operator was used to replace the original up-sampling method, and the extracted features were enlarged to a higher level. The experimental results show that, compared with the original Yolov5s model, the accuracy of the improved algorithm is not only increased by 5.6 percentage points, but also the mAP is increased from 77% to 82.6%, which greatly improves the detection efficiency.
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Index Terms
- Steel Defect Detection Based on Yolov5
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Published In
September 2022
1221 pages
ISBN:9781450396899
DOI:10.1145/3573942
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Association for Computing Machinery
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Publication History
Published: 16 May 2023
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- Research-article
- Research
- Refereed limited
Funding Sources
- Xi?an University of Posts and Telecommunications Key Innovation Fund Project of Science and Technology
Conference
AIPR 2022
AIPR 2022: 2022 5th International Conference on Artificial Intelligence and Pattern Recognition
September 23 - 25, 2022
Xiamen, China
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