Citation

Liu Y, Duan M, Ding G, Ding H, Hu P, Zhao H. Entropy (Basel) 2023; 25(9).

Copyright

(Copyright © 2023, MDPI: Multidisciplinary Digital Publications Institute)

DOI

10.3390/e25091280

PMID

37761579

PMCID

PMC10527674

Abstract

In recent years, the number of traffic accidents caused by road defects has increased dramatically all over the world, and the repair and prevention of road defects is an urgent task. Researchers in different countries have proposed many models to deal with this task, but most of them are either highly accurate and slow in detection, or the accuracy is low and the detection speed is high. The accuracy and speed have achieved good results, but the generalization of the model to other datasets is poor. Given this, this paper takes YOLOv5s as a benchmark model and proposes an optimization model to solve the problem of road defect detection. First, we significantly reduce the parameters of the model by pruning the model and removing unimportant modules, propose an improved Spatial Pyramid Pooling-Fast (SPPF) module to improve the feature signature fusion ability, and finally add an attention module to focus on the key information. The activation function, sampling method, and other strategies were also replaced in this study. The test results on the Global Road Damage Detection Challenge (GRDDC) dataset show that the FPS of our proposed model is not only faster than the baseline model but also improves the MAP by 2.08%, and the size of this model is also reduced by 6.07 M.

Language: en

Keywords

attention module; convolutional neural network; image processing; road defect detection; YOLOv5s