Smart System for Potholes Detection Using Computer Vision with Transfer Learning

Authors : P. S. Ezekiel; O. E. Taylor; D. J. S. Sako

Volume/Issue : Volume 6 - 2021, Issue 7 - July

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Road potholes are extensively enormous primary disappointments out and about surface. They are brought about by withdrawal and extension of the road surface as water saturates into the ground. To guarantee traffic wellbeing, it is critical and important to regularly investigate and fix road potholes. This paper presents a smart system for potholes detection using computer vision with transfer learning. The system starts by acquiring a pothole images as data, preprocessing the data by creating image annotation and image augmentation on the pothole images. We also created sub directories by creating called images and annotation where we placed our training images in the image folder and the annotated files (which was generated in Xml format) into the annotation folder that we created. We then train our model using transfer learning. By transfer learning, we downloaded a pretrained yolov3 weight file trained on a coco dataset for object detection. We then set the batch_size to be equal to 4, with a tensorflow Gpu of version ==1.13.1, number of experiments = 200, and train_from_pre_trained_model = pre-trained-yolov3 (the weight file we downloaded). After training, we evaluated and saved the trained model. We had a training accuracy of about 97%. We carried out a real time pothole detection on a live streaming video using opencv library, where we detected multiple potholes images.

Keywords : Potholes, Computer Vision, Transfer Learning, Yolov3.


Paper Submission Last Date
31 - October - 2023

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