Detecting Patches on Road Pavement Images acquired with 3D Laser Sensors using Object Detection and Deep Learning
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Regular pavement inspections are key to good road maintenance and road defect corrections. Advanced pavement inspection systems such as LCMS (Laser Crack Measurement System) can automatically detect the presence of different defects using 3D lasers. However, such systems still require manual involvement to complete the detection of pavement defects. This paper proposes an automatic patch detection system using object detection technique. To our knowledge, this is the first time state-of-the-art object detection models Faster RCNN, and SSD MobileNet-V2 have been used to detect patches inside images acquired by LCMS. Results show that the object detection model can successfully detect patches inside LCMS images and suggest that the proposed approach could be integrated into the existing pavement inspection systems. The contribution of this paper are (1) an automatic pavement patch detection models for LCMS images and (2) comparative analysis of RCNN, and SSD MobileNet-V2 models for automatic patch detection.
Ibrahim Syed Hassan, Susan McKeever, Dympna O'Sullivan, David Power, Ray McGowan, Kieran Feighan. Detecting Patches on Road Pavement Images acquired with 3D Laser Sensors using Object Detection and Deep Learning. Proceedings of VISAPP, International Conference on Image Processing and Vision Engineering, online, February 2022, DOI: 10.21427/T5MM-F347