<![CDATA[The existence of potholes on the roadways is one of the primary factors that contribute to the occurrence of crashes involving automobiles. In order to find a solution to this issue, a number of different strategies have been explored. Among these methods are the employment of vibration-based sensors, manual reporting to authorities, and laser imaging for the reconstruction of three-dimensional space. The high cost of installation, potential danger during detection, and lack of night vision are just a few of the drawbacks of some of these systems. Researching the feasibility and accuracy of using thermal imaging to the problem of pothole detection is, hence, the goal of this effort. We have collected enough data with pictures of potholes in different weather conditions and used augmentation techniques to it. After this, a novel technique to this problem area that utilises thermal imaging the convolutional neural networks (CNN) method of deep learning was implemented. Also included is a comparison of the researcher's own convolutional neural model to pretrained models. Positive outcomes will follow from this investigation, and it will aid in directing future studies into this novel use of thermal imaging for pothole detection.]]>
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