Automatic recognition and analysis system of asphalt pavement cracks using interleaved low-rank group convolution hybrid deep network and SegNet fusing dense condition random field

Abstract

Pavement crack detection is a key task to ensure driving safety. Manual crack detection is subjective and inefficient. Therefore, it is very important to develop an automatic crack recognition system. However, asphalt pavement crack detection from images is a challenging problem due to the interference of complex noises. Meanwhile, although deep learning-based methods have recently made great progress in crack classification and recognition, there are still difficulties, such as large parameters and low detection efficiency. For this purpose, we develop a novel crack recognition and analysis system. Firstly, crack images are cut by using the overlapping sliding window to establish crack datasets. Then a crack classification algorithm based on interleaved low-rank group convolution hybrid deep network (ILGCHDN) is proposed to recognize cracks and non-cracks. Next, we propose a crack image binarization architecture called SegNet-DCRF, which fuses the SegNet and the dense condition random field (DCRF). Finally, calculate the unidirectional crack width and the web crack area. Moreover, an interactive crack detection software is developed to further display various information on results. Experimental results show that our model is superior to other state-of-the-art algorithms in terms of accuracy, parameters, speed, and anti-interference ability. Also, for cracks with width of 3 mm or more, the relative error is less than 0.02, which better achieves the actual engineering requirements. Besides, the area of web cracks is calculated to comprehensively evaluate the pavement damage level.