Paper
16 February 2022 A lightweight road defect detection method based on multi-scale hybrid feature fusion
Jin Kuang, Dong Liu, Hong Lv, Xinyue Xu, Lingrong Zhang
Author Affiliations +
Proceedings Volume 12083, Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021); 120830V (2022) https://doi.org/10.1117/12.2623433
Event: Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021), 2021, Kunming, China
Abstract
Automatic road defect detection plays a significant role in road upkeep and transportation safety. However, existing approaches still have some shortcomings in detection accuracy, real-time, and hardware requirement. In this paper, we propose a novel anchor-free road defect detection method based on multi-scale hybrid feature fusion. First, we design a lightweight first-order detector to keep more semantic features. Then, we employ a depth separable convolutional layer to reduce the computational complexity. Finally, we propose a hybrid feature fusion framework to improve the feature description capability. Rigorous experimental evaluations on road benchmark data sets demonstrate that our method achieves the highest accuracy and outperforms the YOLO series models. Furthermore, our method has a short inference time of 32ms, which makes it an excellent model in real-time defect detection tasks.
© (2022) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jin Kuang, Dong Liu, Hong Lv, Xinyue Xu, and Lingrong Zhang "A lightweight road defect detection method based on multi-scale hybrid feature fusion", Proc. SPIE 12083, Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021), 120830V (16 February 2022); https://doi.org/10.1117/12.2623433
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KEYWORDS
Roads

Convolution

Defect detection

Data modeling

Sensors

Computer vision technology

Head

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