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A Deep Learning Based Road Distress Visual Inspection System Using Modified U-Net 


Thitirat Siriborvornratanakul


วารสาร Lecture Notes in Computer Science



          Nowadays the number of vehicles on road is increasing incrementally in emerging countries, and there is a need for proactive road health inspection methods that allow non-laborious and frequent road inspection with reasonable cost. Because of the nature of cheap and non-intrusive, vision-based road analysis has become a very popular topic in vision-based civil engineering researches around the world. In this paper, we discuss recent vision-based road distress detection methods, including our previous work and our presented work, for road distress visual inspection.

          This paper is divided into two parts. In the first part, we present non-deep learning based methods that aim to solve this problem of automatic road distress inspection. In the second part, we analyze recent deep learning based methods for road distress visual inspection and other related inspection of structure health monitoring. Finally, we propose our deep learning model called Modified U-Net whose goal is to solve the very difficult task of vision-based road’s crack detection. Along this paper, we also discuss about human factors in developing vision-based solutions for automatic road distress detection.