Enhancing Road Safety with YOLOv8: Deep Learning for Accurate Brake Light Detection

Road accidents, especially rear-end crashes, remain a serious danger to public safety. Existing in-vehicle camera systems designed for brake light detection often face accuracy issues, particularly when confronted with real-world challenges such as different lighting conditions, obstructions by objects, and busy backgrounds. These drawbacks limit the efficacy of Advanced Driver Assistance Systems (ADAS) and autonomous vehicles, which depend on precise and prompt detection of braking actions to ensure safety. To tackle these issues, this paper introduces a brake light detection system based on deep learning that employs the cutting-edge YOLOv8 model. Our approach harnesses the capabilities of deep learning to create a robust and precise detection system. Deep learning algorithms, like YOLOv8, are adept at recognizing complex patterns within extensive datasets, making them well-suited for this purpose. In this research, the YOLOv8 model is trained using a carefully assembled dataset that encompasses a variety of environmental conditions, vehicle positions, and real-world driving scenarios. Through this training, the model acquires the ability to automatically recognize the distinct visual characteristics that differentiate brake lights from other objects in images. This study evaluates the performance of YOLOv8 for brake light detection, concentrating on its capability to provide earlier alerts regarding braking actions and possible road hazards. By greatly enhancing the accuracy and dependability of brake light detection, our system could transform ADAS functionalities and enhance safety in autonomous driving situations. Ultimately, this research promotes road safety innovations, leading toward a future with fewer collisions and a more secure driving environment for everyone.