Pothole Detection and Reporting System Implementation Using Yolov8 and TensorFlow.js - Ruhil, Nidhi; Sahni, Devansh; Anushaka, Anushaka; Wadhwa, Anurag; Sharma, Anjali (International Journal of Computer Sciences and Engineering, 2023)
Potholes present a substantial hazard to both road safety and the structural integrity of vehicles. This paper introduces a novel approach to pothole detection leveraging YOLOv8, an object detection algorithm, and TensorFlow.js. The proposed system aims to detect potholes accurately and swiftly by analysing live video feeds. The trained model exhibits promising performance metrics in pothole detection, with the bounding box precision at 0.822 and the mean Average Precision (mAP) value of 0.847, highlighting the model`s robustness in accurately localizing potholes. The proposed pothole detection system presents a promising solution for proactive road maintenance and safety enhancement. Its efficiency in real-time detection, combined with the adaptability of TensorFlow.js, holds the potential for widespread implementation, contributing to mitigating road hazards and infrastructure maintenance. The use of Tensorflow.js allows JavaScript developers to work with YOLOv8 reducing the dependency on Python for this purpose. The Pothole Detection and Reporting System with YOLOv8 and Tensorflow.js provides quite promising results.