DFL-YOLO: A tunnel crack detection algorithm for feature aggregation in complex scenarios

Xinqi Zheng; Jiaqing Mo; Shiwen Wang

doi:10.1117/12.3050023

8 November 2024 DFL-YOLO: A tunnel crack detection algorithm for feature aggregation in complex scenarios

Xinqi Zheng, Jiaqing Mo, Shiwen Wang

Proceedings Volume 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024); 134160Y (2024) https://doi.org/10.1117/12.3050023
Event: 2024 4th International Conference on Advanced Algorithms and Neural Networks, 2024, Qingdao, China

Abstract

Tunnel crack detection is a critical application area in computer vision. However, existing detection algorithms face challenges such as low recognition rates, slow detection speeds and high costs in complex tunnel environments. To address these issues, we propose a fast tunnel crack detection algorithm model based on YOLOv8n, named DFL-YOLO. This model introduces a novel Feature Aggregation Pyramid Network (FAPN) that effectively handles variable textures and improves the detection success rate of small cracks. It also includes a Lightweight Detail-Enhanced Shared Convolution Detection Head (LDSC), which reduces the number of parameters and detection time through shared convolution calculations, thereby enhancing detection efficiency. Additionally, Detail-Enhanced convolutional composition C2f-DEConv module is incorporated into the C2f structure to enhance the crack feature extraction capability of the backbone network and thus improve the robustness. The improved DFL-YOLO model achieves an accuracy rate of 93.1%, a recall rate of 85% and mAP@50 of 91.7% for tunnel crack detection, while its computational intensity is 43.8% lower than that of the original YOLOv8 model. This makes DFL-YOLO more suitable for deployment on resource-constrained detection devices.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xinqi Zheng, Jiaqing Mo, and Shiwen Wang "DFL-YOLO: A tunnel crack detection algorithm for feature aggregation in complex scenarios", Proc. SPIE 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), 134160Y (8 November 2024); https://doi.org/10.1117/12.3050023

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KEYWORDS

Convolution

Object detection

Detection and tracking algorithms

Feature extraction

Head

Lithium

Ablation

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