BFD-YOLO: A YOLOv7-Based Detection Method for Building Façade Defects
Abstract
:1. Introduction
2. Materials and Methods
2.1. Dataset
2.1.1. Image Acquisition
2.1.2. Image Preprocessing and Data Labeling
2.1.3. Data Augmentation
2.1.4. Objects Information
3. Improved Network
3.1. MobileOne Module
3.1.1. Over-Parametrization Structure
3.1.2. Re-Parametrization Structure
3.1.3. MobileOne Module
3.2. Coordinate Attention Module
3.2.1. Coordinate Information Embedding
3.2.2. Coordinate Attention Generation
3.3. SIoU Loss
3.3.1. Angle Cost
3.3.2. Distance Cost
3.3.3. Shape Cost
3.3.4. IoU Cost
4. Results and Discussion
4.1. Experimental Platform and Parameter Settings
4.2. Evaluation Index
4.3. Detection Effect of Different Defects
4.4. Ablation Experiments
4.5. Comparative Analysis of Different Models
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Defects Type | Number |
---|---|
Delamination | 958 |
Spalling | 1395 |
Tile Loss | 1120 |
Equipment | Name |
---|---|
System | Windows 10 |
CPU | i7-11700 |
GPU | RTX 3060 12 GB |
RAM | 32 GB |
Programming Languages | Python 3.7 |
Deep Learning Framework | Pytorch 1.11.0 |
Types | Precision (%) | Recall (%) | [email protected] (%) | F1 (%) |
---|---|---|---|---|
Delamination | 76.6 | 70.3 | 77.8 | 73.3 |
Spalling | 83.8 | 79.2 | 83.8 | 81.4 |
Tile loss | 84.4 | 83.9 | 85.6 | 84.1 |
Average | 81.6 | 77.8 | 82.4 | 79.7 |
Model Name | Precision (%) | Recall (%) | [email protected] (%) | F1 (%) | Params | FPS |
---|---|---|---|---|---|---|
YOLOv7 | 79.4 | 75.7 | 79.5 | 77.5 | 36,492,560 | 83 |
MobileOne-YOLOv7 | 77.6 | 74.2 | 77.4 | 75.9 | 32,602,512 | 101 |
CA-YOLOv7 | 81.2 | 77.1 | 82.2 | 79.1 | 36,719,104 | 59 |
SIoU-YOLOv7 | 79.3 | 77.0 | 81.5 | 78.1 | 36,492,560 | 81 |
MobileOne-CA-YOLOv7 | 80.7 | 76.4 | 81.8 | 78.5 | 34,140,800 | 77 |
BFD-YOLOv7 | 81.6 | 77.8 | 82.4 | 79.7 | 34,140,800 | 76 |
Methods | Precision (%) | Recall (%) | [email protected] (%) | F1 (%) | FPS |
---|---|---|---|---|---|
BFD-YOLO | 81.6 | 77.8 | 82.4 | 79.7 | 76 |
YOLOv5l | 76.2 | 73.4 | 77.9 | 74.8 | 64 |
RetinaNet | 73.1 | 70.5 | 73.8 | 71.8 | 35 |
Faster R-CNN | 75.4 | 73.7 | 77.2 | 74.5 | 17 |
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Wei, G.; Wan, F.; Zhou, W.; Xu, C.; Ye, Z.; Liu, W.; Lei, G.; Xu, L. BFD-YOLO: A YOLOv7-Based Detection Method for Building Façade Defects. Electronics 2023, 12, 3612. https://doi.org/10.3390/electronics12173612
Wei G, Wan F, Zhou W, Xu C, Ye Z, Liu W, Lei G, Xu L. BFD-YOLO: A YOLOv7-Based Detection Method for Building Façade Defects. Electronics. 2023; 12(17):3612. https://doi.org/10.3390/electronics12173612
Chicago/Turabian StyleWei, Guofeng, Fang Wan, Wen Zhou, Chengzhi Xu, Zhiwei Ye, Wei Liu, Guangbo Lei, and Li Xu. 2023. "BFD-YOLO: A YOLOv7-Based Detection Method for Building Façade Defects" Electronics 12, no. 17: 3612. https://doi.org/10.3390/electronics12173612