Opto-Electronic Engineering, Volume. 51, Issue 11, 240171-1(2024)
Improved YOLOv8 algorithm for detecting cracks in roadbed slopes
Figures & Tables(20)
Fig. 13. Comparison of slope crack detection results. (a) YOLOv8n; (b) Ours
Fig. 14. Comparison results of training on RDD2022 dataset. (a) Loss convergence curve; (b) mAP50 curve
Fig. 15. Comparison of detection results of different algorithms on RDD2022 dataset
Table 1. Results of ablation experiments
View tableView in ArticleTable 1. Results of ablation experiments
Model C2f-RVB C2f-GD L-GNHead SIoU mAP50/% mAP50-95/% Params/M GFLOPs FPS YOLOv8n 85.1 43.2 3.00 8.1 96.6 YOLOv8n_1 √ 86.5 44.6 2.64 7.0 86.8 YOLOv8n_2 √ 86.4 44.2 2.63 7.1 93.8 YOLOv8n_3 √ 86.1 43.9 2.36 6.5 103.1 YOLOv8n_4 √ 85.7 43.4 3.00 8.1 97.9 YOLOv8n_5 √ √ √ 87.9 46.0 1.95 5.6 99.4 YOLOv8n_6 √ √ 87.2 43.1 2.24 6.1 106.9 YOLOv8n_7 √ √ √ 87.6 45.3 1.60 4.5 104.0 YOLOv8n_8 √ √ √ √ 88.4 45.7 1.60 4.5 114.7
Table 2. Comparison of the experimental results
View tableView in ArticleTable 2. Comparison of the experimental results
Model mAP50/% mAP50-95/% Params/M GFLOPs FPS SSD 69.2 34.6 63.82 124.5 96.6 Faster-RCNN 70.4 37.8 137.42 371.4 18.7 RT-DETR-L[ 32 ]81.6 39.8 28.44 100.6 32.8 YOLOv5n 82.4 41.3 2.50 7.1 110.1 YOLOv7tiny 81.5 39.5 6.12 12.4 109.8 YOLOv8n 85.1 43.2 3.00 8.1 96.6 YOLOv9s[ 33 ]87.9 44.6 7.33 26.8 64.7 文献[ 34 ]88.7 44.9 89.1 15.4 — 文献[ 35 ]87.8 45.5 33.4 — — Ours 88.4 45.7 1.60 4.5 114.7
Table 3. Comparison of SE at different positions
View tableView in ArticleTable 3. Comparison of SE at different positions
P/% R/% mAP50/% Params/M YOLOv8n 90.2 83.0 85.1 3.00 ① 89.5 82.9 84.9 2.64 ② 88.5 81.7 82.1 2.64 ③ 88.4 81.1 85.8 2.64 Ours 91.0 83.5 86.5 2.64
Table 4. Comparison results of different loss functions
View tableView in ArticleTable 4. Comparison results of different loss functions
Loss P/% R/% mAP50/% Params/M CIoU 90.2 83.0 85.1 3.0 EIoU 89.5 82.6 83.8 3.0 WIoU 89.9 84.1 84.2 3.0 SIoU 89.9 84.2 85.7 3.0
Table 5. Comparison of the experimental results on RDD2022 dataset
View tableView in ArticleTable 5. Comparison of the experimental results on RDD2022 dataset
Model mAP50/% mAP50-95/% Params/M GFLOPs FPS Faster-RCNN 63.1 34.5 137.42 371.4 18.2 RT-DETR-L[ 32 ]73.1 40.4 28.44 100.6 32.4 YOLOv5n 75.9 44.2 2.50 7.1 107.8 YOLOv7tiny 75.5 44.1 6.12 12.4 108.5 YOLOv8n 76.3 44.9 3.00 8.1 95.8 YOLOv9s[ 33 ]77.9 44.6 7.33 26.8 65.2 文献[ 34 ]78.4 45.1 89.1 15.4 — 文献[ 35 ]78.1 44.5 33.4 — — Ours 78.9 44.7 1.60 4.5 111.6
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Xiaofu Niu, He Huang, Hongmin Zhang, Tiefeng Xu. Improved YOLOv8 algorithm for detecting cracks in roadbed slopes[J]. Opto-Electronic Engineering, 2024, 51(11): 240171-1
Paper Information
Category: Article
Received: Jul. 20, 2024
Accepted: Oct. 24, 2024
Published Online: Jan. 24, 2025
The Author Email: Hongmin Zhang (张红民)
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