POTHOLE DETECTION SYSTEM USING MACHINE LEARNING

Abstract

Potholes represent a ubiquitous road hazard, posing risks to both vehicular safety and infrastructure integrity. Addressing this challenge requires efficient and automated detection systems. Leveraging advancements in machine learning (ML), this paper proposes a pothole detection system using ML techniques. The system employs feature extraction, convolutional neural networks, transfer learning, and semantic segmentation for robust and accurate detection of potholes from image data. The proposed system contributes to the automation of road maintenance processes, enabling timely repairs and enhancing road safety. Through extensive experimentation and evaluation, the system demonstrates promising results in terms of detection accuracy and efficiency. Furthermore, discussions on integration with smart city infrastructure and autonomous vehicles highlight its potential impact on enhancing transportation systems' resilience and safety. This paper provides insights into the development and deployment of ML-based pothole detection systems, paving the way for improved road maintenance strategies and safer travel experiences. Key Words: Feature extraction, Convolutional Neural Network, Transfer learning, Semantic segmentation