In recent years, technology has contributed to improving transportation systems, but the maintenance and upkeep of road networks remain a challenge despite these advancements. Potholes, cracks, and other road defects can lead to accidents, traffic congestion, and expensive repairs. An android pothole detection system that utilizes smartphone sensors and machine learning algorithms has the potential to revolutionize road maintenance and safety. This proposed system will use a smartphone camera to detect potholes and distinguish them from other road irregularities. It will also be integrated with a backend database that can store and analyze data on road conditions, enabling authorities to prioritize maintenance and repairs. The android pothole detection system can offer several benefits, including reducing road accidents, lowering repair costs, and minimizing traffic congestion. Our proposed solution aims to crowdsource information from people who face these issues and forward it to relevant authorities using an Android application. To achieve this, we will utilize a Deep Learning model capable of detecting potholes, collecting information from users, and sending it to authorities. The success of this solution depends on the accuracy of the Deep Learning model, the quality of user-provided information, the responsiveness of relevant authorities, and user engagement. Therefore, the system must have appropriate parameters to manage these factors and guarantee the solution's effectiveness. In conclusion, utilizing technology for android pothole detection can lead to effective and timely repairs, contributing to overall road safety and reducing vehicle damage. The proposed android pothole detection system brings people together to work on a common problem and has the potential to revolutionize road maintenance and safety, providing safer and more efficient means of travel for everyone.

Keywords : Deep Learning , Road Safety , Efficient Means To Travel , Smartphone Sensors.