Promoting sustainable urban mobility via automated sidewalk defect detection

Abstract

Encouraging sustainable mobility through sidewalk condition improvement is a critical concern for urban transportation. Sidewalk condition affects pedestrian safety, satisfaction, and mobility inclusiveness. Early sidewalk defect detection and repair ensure transport justice by addressing pedestrian inequality caused by walkability issues. This study presents novel Sidewalk Defect Detection Models (SDDMs) using computer vision to identify and delineate sidewalk defect boundaries accurately. The SDDMs provide a cost-effective and efficient sidewalk inspection method, achieving high accuracy in recognizing defects for concrete and brick materials (mIoU of 0.91 and mAP of 0.99 for concrete, mIoU of 0.90, and mAP of 0.97 for brick). Integrated with Google Street View for data acquisition, it offers a rapid solution for monitoring sidewalk conditions remotely, promoting sustainability through timely repairs. This research provides significant advancements in urban planning and transport research, ultimately improving pedestrian safety and satisfaction. Thus, it makes human settlements more inclusive, safe, and sustainable.