Strip steel surface defect detection based on lightweight YOLOv5
Deep learning-based methods for detecting surface defects on strip steel have advanced detection capabilities, but there are still problems of target loss, false alarms, large computation, and imbalance between detection accuracy and detection speed. In order to achieve a good balance between detect...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-10-01
|
| Series: | Frontiers in Neurorobotics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnbot.2023.1263739/full |
| _version_ | 1797666519658790912 |
|---|---|
| author | Yongping Zhang Sijie Shen Sen Xu |
| author_facet | Yongping Zhang Sijie Shen Sen Xu |
| author_sort | Yongping Zhang |
| collection | DOAJ |
| description | Deep learning-based methods for detecting surface defects on strip steel have advanced detection capabilities, but there are still problems of target loss, false alarms, large computation, and imbalance between detection accuracy and detection speed. In order to achieve a good balance between detection accuracy and speed, a lightweight YOLOv5 strip steel surface defect detection algorithm based on YOLOv5s is proposed. Firstly, we introduce the efficient lightweight convolutional layer called GSConv. The Slim Neck, designed based on GSConv, replaces the original algorithm's neck, reducing the number of network parameters and improving detection speed. Secondly, we incorporate SimAM, a non-parametric attention mechanism, into the improved neck to enhance detection accuracy. Finally, we utilize the SIoU function as the regression prediction loss instead of the original CIoU to address the issue of slow convergence and improve efficiency. According to experimental findings, the YOLOv5-GSS algorithm outperforms the YOLOv5 method by 2.9% on the NEU-DET dataset and achieves an average accuracy (mAP) of 83.8% with a detection speed (FPS) of 100 Hz, which is 3.8 Hz quicker than the YOLOv5 algorithm. The proposed model outperforms existing approaches and is more useful, demonstrating the efficacy of the optimization strategy. |
| first_indexed | 2024-03-11T19:59:34Z |
| format | Article |
| id | doaj.art-80fc0b60af4041fe94fb30061f9a1138 |
| institution | Directory Open Access Journal |
| issn | 1662-5218 |
| language | English |
| last_indexed | 2024-03-11T19:59:34Z |
| publishDate | 2023-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurorobotics |
| spelling | doaj.art-80fc0b60af4041fe94fb30061f9a11382023-10-04T09:59:06ZengFrontiers Media S.A.Frontiers in Neurorobotics1662-52182023-10-011710.3389/fnbot.2023.12637391263739Strip steel surface defect detection based on lightweight YOLOv5Yongping ZhangSijie ShenSen XuDeep learning-based methods for detecting surface defects on strip steel have advanced detection capabilities, but there are still problems of target loss, false alarms, large computation, and imbalance between detection accuracy and detection speed. In order to achieve a good balance between detection accuracy and speed, a lightweight YOLOv5 strip steel surface defect detection algorithm based on YOLOv5s is proposed. Firstly, we introduce the efficient lightweight convolutional layer called GSConv. The Slim Neck, designed based on GSConv, replaces the original algorithm's neck, reducing the number of network parameters and improving detection speed. Secondly, we incorporate SimAM, a non-parametric attention mechanism, into the improved neck to enhance detection accuracy. Finally, we utilize the SIoU function as the regression prediction loss instead of the original CIoU to address the issue of slow convergence and improve efficiency. According to experimental findings, the YOLOv5-GSS algorithm outperforms the YOLOv5 method by 2.9% on the NEU-DET dataset and achieves an average accuracy (mAP) of 83.8% with a detection speed (FPS) of 100 Hz, which is 3.8 Hz quicker than the YOLOv5 algorithm. The proposed model outperforms existing approaches and is more useful, demonstrating the efficacy of the optimization strategy.https://www.frontiersin.org/articles/10.3389/fnbot.2023.1263739/fulldefect detectiontarget detectionGSConvSimAMloss function |
| spellingShingle | Yongping Zhang Sijie Shen Sen Xu Strip steel surface defect detection based on lightweight YOLOv5 Frontiers in Neurorobotics defect detection target detection GSConv SimAM loss function |
| title | Strip steel surface defect detection based on lightweight YOLOv5 |
| title_full | Strip steel surface defect detection based on lightweight YOLOv5 |
| title_fullStr | Strip steel surface defect detection based on lightweight YOLOv5 |
| title_full_unstemmed | Strip steel surface defect detection based on lightweight YOLOv5 |
| title_short | Strip steel surface defect detection based on lightweight YOLOv5 |
| title_sort | strip steel surface defect detection based on lightweight yolov5 |
| topic | defect detection target detection GSConv SimAM loss function |
| url | https://www.frontiersin.org/articles/10.3389/fnbot.2023.1263739/full |
| work_keys_str_mv | AT yongpingzhang stripsteelsurfacedefectdetectionbasedonlightweightyolov5 AT sijieshen stripsteelsurfacedefectdetectionbasedonlightweightyolov5 AT senxu stripsteelsurfacedefectdetectionbasedonlightweightyolov5 |