Redefining Road Safety: A Comprehensive Literature Review on Computer Vision for Speed Breaker Detection in Vehicle

Abstract

This literature review focuses on the use of computer vision techniques for detecting speed breakers on roads, with the objective of improving road safety. Speed breakers play a crucial role in controlling vehicle speeds and ensuring pedestrian safety, but their sudden appearance can lead to accidents, discomfort, and damage to vehicles. The review highlights five key points derived from the analysis of existing literature. Firstly, various computer vision algorithms, including edge detection, object recognition, and machine learning approaches, have been employed for accurate speed breaker detection. Secondly, different data sources such as onboard cameras, LIDAR sensors, and GPS systems have been utilized to gather road information and identify speed breakers. Thirdly, evaluation metrics and benchmark datasets have been developed to assess the effectiveness and robustness of speed breaker detection algorithms. Fourthly, efforts have been made to implement real-time computer vision systems for speed breaker detection, enabling timely warnings and adaptive vehicle control. Lastly, the review identifies challenges such as adverse weather conditions, occlusions, and generalization to different road environments and proposes potential research directions to address these limitations. Overall, this review contributes to the understanding of computer vision's advancements in enhancing road safety through speed breaker detection.