Opto-Electronic Engineering, Volume. 51, Issue 10, 240170(2024)
MAS-YOLOv8n road damage detection algorithm from the perspective of drones
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Fig. 2. Multi-branch hybrid attention mechanism (MBMA module) structure
Fig. 3. C2f structure improvement. (a) Original C2f structure; (b) Improved C2f structure
Fig. 11. Experimental results before and after improvement of the label allocation algorithm. (a) Comparison of Loss value changes; (b) Comparison of mAP changes
Table 1. Dataset road damage details
View tableView in ArticleTable 1. Dataset road damage details
Damage type Detail Class name Number of China-Drone Number of dataset1 Number of dataset2 Crack Longitudinal crack D00 1426 3995 2678 Lateral crack D10 1263 3979 1096 Alligator crack D20 293 6199 641 Other corruption Rutting,bump,pothole,separation D40 86 2243 235 Crosswalk blur D43 — 736 — White line blur D44 — 3995 — Special signs Manhole cover D50 — 3553 — Repair Repair 769 — 277
Table 2. Experimental environment configuration
View tableView in ArticleTable 2. Experimental environment configuration
Category Environment condition CPU AMD Ryzen 7 5800X 8-Core Processor GPU NVIDIA GeForce RTX 3060 Graphics memory 12 G Operating system Ubuntu 22.04 CUDA version CUDA 12.0 Scripting language Python
Table 3. Comparative experimental results
View tableView in ArticleTable 3. Comparative experimental results
Model China-Drone mAP@0.5/% Dataset1 mAP@0.5/% Dataset2 mAP@0.5/% Parameter/M Model volume/MB YOLOv5n 64.7 64.0 92.2 2.5 5.03 YOLOv8n 68.5 64.7 93.6 3.0 5.96 YOLOv10n 62.4 61.8 91.4 2.7 5.51 GOLD-YOLO 66.1 65.9 94.5 7.2 11.99 Faster-RCNN 67.8 66.4 94.7 34.6 310.24 TOOD 69.0 65.6 94.9 28.3 243.95 RTMDet-Tiny 65.6 64.1 93.0 4.4 77.76 RT-DETR 68.2 67.2 87.5 20.0 308 MAS-YOLOv8n 71.6 67.3 95.3 3.2 5.96
Table 4. Verification results of attention mechanism
View tableView in ArticleTable 4. Verification results of attention mechanism
Attention mechanism China-Drone mAP@0.5/% Dataset1 mAP@0.5/% Dataset2 mAP@0.5/% Parameter/M — 68.5 64.7 93.6 3.0 SE 69.1 64.1 93.5 3.1 CMBA 67.4 65.5 94.5 3.2 CA 68.8 65.7 94.5 3.2 MBMA 70.7 66.7 94.8 3.2
Table 5. Results of the ablation experiment
View tableView in ArticleTable 5. Results of the ablation experiment
Model China-Drone mAP@0.5/% Dataset1 mAP@0.5/% Dataset2 mAP@0.5/% Parameter/M GFLOPS Model volume/MB FPS 1 YOLOv8n 68.5 64.7 93.6 3.0 8.1 5.96 137 2 +MBMA 70.7 66.7 94.8 3.2 8.1 5.96 116 3 +ShapeIoU 70.9 67.0 95.0 3.0 8.1 5.96 135 4 MAS-YOLOv8n 71.6 67.3 95.3 3.2 8.1 5.96 114
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Xiaoyan Wang, Xiyu Wang, Jie Li, Wenhui Liang, Jianhong Mou, Churan Bi. MAS-YOLOv8n road damage detection algorithm from the perspective of drones[J]. Opto-Electronic Engineering, 2024, 51(10): 240170
Paper Information
Category: Article
Received: Jul. 18, 2024
Accepted: Sep. 10, 2024
Published Online: Jan. 2, 2025
The Author Email: Jie Li (李杰)
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