Pit Detection Using Convolutional Neural Networks

The subject of driving safety is of paramount importance, as it is susceptible to significant risks that can arise from a momentary lapse in attention. In order to achieve this, an application has been developed with the objective of enhancing preventive driving techniques and mitigating the occurrence of adverse events within the context of traffic. The real-time pothole detection application was developed with the objective of assisting drivers in avoiding dangerous road conditions, thus facilitating safe and comfortable driving. The model trained to detect potholes demonstrated robust performance, achieving an average accuracy of 89% and an average recovery rate of 85%. This indicates a high level of ability to correctly classify potholes and identify as many of them as possible. The F1 score, which balances accuracy and retrieval rate, was 0.862, indicating a robust overall performance. The model was trained for a period of nine hours and demonstrated the capacity to effectively identify potholes in a range of conditions. The program is currently capable of detecting potholes on the road, thereby providing a straightforward and efficacious solution for their avoidance.