Crackdet: An Improved Deep Learning Framework Base on Multi-Scale Convolutional Architecture for Detecting Road Cracks

Idris Ya'u Idris, Badamasi Imam Ya'u, Usman Ali

Abstract


Highway is not only the carrier of transportation, but also the link of regional economy. However, with the longer service time of the pavement, various defects such as cracks, potholes, and deformation will appear on the road surface one after another. Traditionally, crack evaluation is conducted manually through human field surveys. Though, these manual survey methods have poor repeatability and reproducibility, need excessive time, consume great amounts of labor, and put surveyors in hazardous situations. In this research, we propose an improved deep learning framework base on multi-scale convolutional architecture for detecting road cracks. Firstly, deconvolution and fusion of CNN feature maps are proposed to add context and deeper features for better crack detection at low feature map scales. In addition, soft non-maximal suppression (NMS) is applied across cracks proposals at different feature scales to address the object occlusion challenge.


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