Authors - Al John A. Villareal, Jaime M. Samaniego Abstract - Potholes significantly impact road safety, vehicle performance, and infrastructure maintenance, particularly in developing countries where monitoring systems remain largely manual. This study presents the design and implementation of an Android-based mobile application that utilizes sensor fusion and deep learning for real-time pothole detection and severity analysis. The system integrates a YOLO-based object detection model with smartphone sensors, including accelerometer, gyroscope, and Global Positioning System (GPS), enabling simultaneous visual and motion-based detection. A dataset consisting of 9,253 road surface images containing 16,123 pothole annotations was used for training and evaluation using a 70:20:10 dataset split for training, validation, and testing. Among the evaluated models, YOLO11s achieved the highest mAP@50– 95 value of 54.2%. However, YOLO26n was selected and implemented in the developed Android application due to its competitive detection performance, compact 5.2 MB model size, and suitability for real-time mobile deployment. Field testing across four road segments covering 18.97 kilometers resulted in 130 detections, of which 84 were verified potholes and 46 were false detections, yielding a verification rate of 64.62% and a false detection rate of 35.38%. The system recorded an average detection density of 6.85 potholes per kilometer. Results demonstrate that integrating deep learning and sensor fusion in a mobile platform provides a scalable and cost-effective solution for automated road condition monitoring and intelligent transportation systems.
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