Complete Panoptic Traffic Recognition System with Ensemble of YOLO Family Models


Authors : Deniz Sen

Volume/Issue : Volume 8 - 2023, Issue 10 - October

Google Scholar : http://tinyurl.com/42a26rhv

Scribd : http://tinyurl.com/3prm7dx5

DOI : https://doi.org/10.5281/zenodo.10390839

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.

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.

Never miss an update from Papermashup

Get notified about the latest tutorials and downloads.

Subscribe by Email

Get alerts directly into your inbox after each post and stay updated.
Subscribe
OR

Subscribe by RSS

Add our RSS to your feedreader to get regular updates from us.
Subscribe