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A Modified U-Net Architecture for Road Surfaces Cracks Detection

Cracks on road surfaces causes inconveniences to drivers and passengers and may cause mechanical failure or even accidents. Good Road condition plays an important role in quick transportation of goods and services from one place to another and acts as a catalyst for the economic development. Road surfaces need to be maintained in good condition to ensure the safety of road users. Road damage detection is important for Structural Health Monitoring (SHM). Traditional manual inspection is normally performed through human visualization which is time consuming, expensive, dangerous because of the passing vehicles, suffers from subjective judgment of the inspector and pose difficulties in keeping records for future road maintenance and repair. The rapid emergency and development of AI has stimulated many experts to automate the process of crack detection through computer vision (CV) technology, though most of these studies faces challenge on getting good detection accuracy. In this study a novel modified U-Net Architecture for image classification and segmentation is proposed to detect cracks on the road surfaces by using detection and classification of the road images to determine whether they represent cracks or not. Extensive experiments are conducted on three publicly available road crack datasets to evaluate the performance of our proposed model, The performance of the proposed Modified U-Net architecture was verified with respect to different performance metrics such as accuracy, precision, recall and f1 score. Qualitative and Quantitative comparisons experimental results of the proposed approach were also compared with existing state of the art U-Net architectures. It can be inferred from results that the proposed approach achieves superior performance in terms of detection accuracy.