Abstract: Potholes present an ongoing hazard to the safety of roads, resulting in collisions, harm to vehicles, and deterioration of infrastructure. To tackle this difficulty, it is necessary to develop inventive systems that can promptly identify potholes in order to minimize the risks involved. This research presents PotholeWatch, an innovative method for detecting potholes that is based on convolutional neural networks (CNNs). The methodology we employ takes advantage of a distinctive dataset that includes road photos with annotations, allowing for effective training and assessment of the model. PotholeWatch offers effective real-time performance necessary for deployment in vehicle situations through careful preprocessing and model architecture design. Rigorous testing confirms the system's precision and promptness, showcasing its capacity to transform road safety. PotholeWatch is a technology that works well with vehicle safety systems. It provides proactive notifications to drivers, which helps provide a safer driving experience. This study introduces PotholeWatch as an innovative solution that utilizes Convolutional Neural Networks (CNNs) to address road hazards. It aims to improve the resilience of infrastructure and prevent accidents.

| DOI: 10.17148/IARJSET.2024.114105