Publication Date: 2023/12/15
Abstract: Erroneous driver behavior is one of the most dangerous aspects of driving in traffic, which can be prevented if the dangerous actions are detected immediately and the necessary actions are taken. However, in order to be able to implement a hazard detection framework, we first need a model that can perceive the related objects in the traffic scene, which is not a trivial task considering the diversity of the outside conditions. In this paper, we propose a computer vision- based system that only requires dash camera footage to compute binary drivable area segmentation, dashed and straight lane line segmentation, and traffic object detection. We utilize two YOLOv5s and YOLOP, a multitask architecture, to create a complete panoptic perception model and train the member models using the public BDD100K, traffic sign dataset, and our private dataset. To reduce the significant annotation overhead of the segmentation tasks, we use semi- supervised learning techniques and a different annotation approach for lane line labeling. We also present 2 lane violation detection algorithms and temporal smoothing techniques for the segmentation tasks. We managed to achieve remarkable results in all 3 of our tasks and showed the usability of our system under real-world scenarios.
Keywords: YOLOP; Lane Detection; Traffic Object Detection; Drivable Area Segmentation; Traffic Violation.
DOI: https://doi.org/10.5281/zenodo.10390839
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT23OCT1838.pdf
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