Authors - Aswini N, Kavitha D Abstract - Obstacle detection is vital for safe navigation in autonomous driving; however, adverse weather conditions like fog, rain, low light, and snow can compromise image quality and reduce detection accuracy. This paper presents a pipeline to enhance image quality under extreme conditions using traditional image processing techniques, followed by obstacle detection with the You Only Look Once (YOLO) deep learning model. Initially, image quality is improved using Contrast Limited Adaptive Histogram Equalization (CLAHE) followed by bilateral filtering to enhance visibility and preserve edge details. The enhanced images are then processed by pre-trained YOLO v7 model for obstacle detection. This approach highlights the effectiveness of integrating traditional enhancement techniques with deep learning for robust obstacle detection, even under adverse weather, offering a promising solution for enhancing autonomous vehicle reliability.
Open Zoom
