BD-YOLOv8s: enhancing bridge defect detection with multidimensional attention and precision reconstruction

Wenyuan Xu ¹, Xiang Li ¹, Yongcheng Ji ²

⁰Northeast Forestry University, Harbin, China.

Scientific Reports +

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August 12, 2024

View abstract on PubMed

Summary

This study introduces BD-YOLOv8s, an improved object detection method for concrete bridge defects. It significantly enhances accuracy by reducing missed detections and false positives in complex bridge imagery.

Area Of Science

Computer Vision
Structural Health Monitoring
Artificial Intelligence

Background

Object detection networks like YOLO show promise but struggle with bridge defect detection due to complex backgrounds and variable defect appearances.
Existing methods face challenges including low accuracy, missed detections, and false positives in bridge imagery.
Accurate detection of concrete bridge defects is crucial for infrastructure maintenance and safety.

Purpose Of The Study

To develop an advanced object detection methodology, BD-YOLOv8s, specifically tailored for concrete bridge defect detection.
To enhance the accuracy and reliability of detecting bridge defects by addressing limitations of existing YOLO models.
To improve the robustness of defect detection algorithms in challenging real-world bridge environments.

Main Methods

Utilized YOLOv8s as the baseline architecture for bridge defect detection.
Integrated ODConv (Orthogonal Deep Convolution) into the second convolutional layer to enhance feature extraction.
Incorporated the CBAM (Convolutional Block Attention Module) into C2F modules to leverage spatial and channel attention mechanisms.
Replaced traditional upsampling with CARAFE (Content-Aware Reassembly of Features) for improved feature map reconstruction.

Main Results

BD-YOLOv8s achieved a mean Average Precision (mAP) of 86.2% at IoU threshold 0.5.
The model obtained 56% mAP@0.5:0.95, indicating strong performance across various overlap thresholds.
BD-YOLOv8s demonstrated significant improvements over the baseline, with increases of 5.3% and 5.7% in mAP@0.5 and mAP@0.5:0.95, respectively.
The proposed method substantially reduced false positives and missed detections.

Conclusions

BD-YOLOv8s offers a superior approach for concrete bridge defect detection compared to standard YOLOv8s.
The integration of ODConv, CBAM, and CARAFE effectively enhances the model's ability to handle complex bridge defect imagery.
The improved accuracy and reduced detection errors make BD-YOLOv8s a valuable tool for structural health monitoring of bridges.