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One of the most important tasks in road maintenance is the detection of potholes. This process is usually done through manual visual inspection, where certified engineers assess recorded images of pavements acquired using cameras or professional road assessment vehicles. Machine learning techniques are now being applied to this problem, with models trained to automatically identify road conditions. However, approaching this real-world problem with machine learning techniques presents the classic problem of how to produce generalizable models. Images and videos may be captured in different illumination conditions, with different camera types, camera angles and resolutions. In this paper we present our approach to building a generalized learning model for pothole detection. We apply four datasets that contain a range of image and environment conditions. Using the Faster RCNN object detection model, we demonstrate the extent to which pothole detection models can generalize across various conditions. Our work is a contribution to bringing automated road maintenance techniques from the research lab into the real-world.
Hassan, S. I., O'Sullivan, D., & Mckeever, S. (2021). Pothole Detection under Diverse Conditions using Object Detection Models. IMPROVE, 1, 128-136. DOI: 10.21427/8QXS-W574
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This work is licensed under a Creative Commons Attribution-Share Alike 4.0 License.
SFI Centre for Research Training in Machine Learning (ML-LABS)
IMPROVE 2021 : International Conference on Image Processing and Vision Engineering