Opto-Electronic Engineering, Volume. 52, Issue 5, 250032(2025)
Steel surface defect detection based on YOLOv8-SOE
Figures & Tables(16)
Fig. 7. Feature heatmap. (a) Original image; (b) YOLOv8n feature heatmap; (c) YOLOv8-SOE feature heatmap
Fig. 9. (a) Sample images processed by Gaussian noise;(b) Sample images after Gaussian blur processing
Table 1. Experiment results on FSCConv at different positions
View tableView in ArticleTable 1. Experiment results on FSCConv at different positions
Scheme Precision Recall FLOPs/G FPS mAP/% Baseline model 0.759 0.722 8.1 218.2 75.3 A 0.827 0.677 11.6 112.2 77.0 B 0.754 0.768 9.1 187.9 79.4
Table 2. Experiment results on CSP-OK module at different positions
View tableView in ArticleTable 2. Experiment results on CSP-OK module at different positions
Scheme Precision Recall FLOPs/G FPS mAP/% Baseline model 0.759 0.722 8.1 218.2 75.3 C 0.766 0.779 19.9 118.8 79.1 D 0.762 0.743 9.8 116.6 79.1
Table 3. Results of ablation experiments
View tableView in ArticleTable 3. Results of ablation experiments
FSCConv CSP-OK SIoU Precision Recall FLOPs/G FPS mAP/% rm/% rf/% — — — 0.759 0.722 8.1 218.2 75.3 27.33 21.78 √ — — 0.760 0.762 9.1 185.3 79.2 26.18 21.40 √ — √ 0.754 0.768 9.1 187.9 79.4 24.89 20.48 — √ — 0.762 0.743 9.8 116.6 79.1 25.72 23.79 — √ √ 0.731 0.786 9.8 137.2 79.3 21.58 26.89 — — √ 0.738 0.761 8.1 215.7 77.4 23.88 26.21 √ √ — 0.783 0.746 11.8 164.0 80.4 24.93 19.41 √ √ √ 0.739 0.767 11.8 172.0 80.7 21.48 23.79
Table 4. Results of comparative experiments
View tableView in ArticleTable 4. Results of comparative experiments
Model AP/% FLOPs/G FPS mAP/% Cr In Pa Ps Rs Sc Faster-RCNN 37.2 84.1 89.7 82.3 72.7 93.2 134.0 34.200 76.5 RT-DETR 44.5 77.9 89.0 67.5 56.1 92.2 57.0 48.100 72.5 YOLOv5s 53.4 79.8 93.2 81.1 51.5 96.0 15.8 74.505 75.8 YOLOv7 57.2 84.6 91.1 84.9 54.8 93.3 103.2 65.790 77.7 YOLOX 30.2 77.5 85.2 75.2 39.0 88.8 13.32 43.200 66.6 YOLOv8n 44.1 75.7 91.9 82.7 61.0 96.3 8.1 218.200 75.3 YOLOv10n 42.2 78.2 88.9 76.9 55.3 86.5 6.5 192.500 71.3 GBS-YOLOv7t[ 24 ]32.7 69.6 92.4 96.5 57.7 88.6 — 104.1 72.9 PIC2f-YOLO[ 25 ]— — — — — — 10.6 80 78.0 RFB-YOLOv5-E[ 26 ]52.1 75.5 95.5 97.3 62.3 92.4 22.4 122 79.2 Ref. [ 27 ]39.5 86.0 92.1 78.9 62.6 85.3 12.2 — 74.1 Ours 60.0 85.6 93.1 84.8 62.2 98.2 11.8 172.0 80.7
Table 5. Comparative experimental results under different lighting environments
View tableView in ArticleTable 5. Comparative experimental results under different lighting environments
Dataset Model Precision Recall mAP/% NEU-DET YOLOv8n 0.759 0.722 75.3 Ours 0.739 0.767 80.7 NEU-DET-brightness YOLOv8n 0.730 0.672 74.9 Ours 0.757 0.745 79.6
Table 6. Comparative experimental results in fuzzy and noisy environments
View tableView in ArticleTable 6. Comparative experimental results in fuzzy and noisy environments
Dataset Model Precision Recall mAP/% NEU-DET YOLOv8n 0.759 0.722 75.3 Ours 0.739 0.767 80.7 NEU-DET-augmented YOLOv8n 0.644 0.688 72.7 Ours 0.789 0.726 78.0
Table 7. Comparative experiment results on VOC2012 dataset
View tableView in ArticleTable 7. Comparative experiment results on VOC2012 dataset
Model Percision Recall mAP/% YOLOv5s 0.724 0.646 68.2 YOLOX 0.715 0.608 61.6 YOLOv10n 0.702 0.536 60.3 YOLOv8n 0.689 0.569 63.0 Ours 0.754 0.621 69.4
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Yujie Ma, Chuqing Cao, Jing Zhang. Steel surface defect detection based on YOLOv8-SOE[J]. Opto-Electronic Engineering, 2025, 52(5): 250032
Paper Information
Category: Article
Received: Feb. 13, 2025
Accepted: Apr. 3, 2025
Published Online: Jul. 18, 2025
The Author Email: Chuqing Cao (曹雏清)
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