Multi-lane Detection Using Instance Segmentation and Attentive Voting

• URL: http://arxiv.org/abs/2001.00236v1
• Date: Wed, 1 Jan 2020 16:48:42 GMT
• Title: Multi-lane Detection Using Instance Segmentation and Attentive Voting
• Authors: Donghoon Chang (1), Vinjohn Chirakkal (2), Shubham Goswami (3), Munawar Hasan (1), Taekwon Jung (2), Jinkeon Kang (1,3), Seok-Cheol Kee (4), Dongkyu Lee (5), Ajit Pratap Singh (1) ((1) Department of Computer Science, IIIT-Delhi, India, (2) Springcloud Inc., Korea, (3) Center for Information Security Technologies (CIST), Korea University, Korea, (4) Smart Car Research Center, Chungbuk National University, Korea, (5) Department of Smart Car Engineering, Chungbuk National University, Korea)
• Abstract summary: We propose a novel solution to multi-lane detection, which outperforms state of the art methods in terms of both accuracy and speed. We are able to obtain a lane segmentation accuracy of 99.87% running at 54.53 fps (average)
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Autonomous driving is becoming one of the leading industrial research areas. Therefore many automobile companies are coming up with semi to fully autonomous driving solutions. Among these solutions, lane detection is one of the vital driver-assist features that play a crucial role in the decision-making process of the autonomous vehicle. A variety of solutions have been proposed to detect lanes on the road, which ranges from using hand-crafted features to the state-of-the-art end-to-end trainable deep learning architectures. Most of these architectures are trained in a traffic constrained environment. In this paper, we propose a novel solution to multi-lane detection, which outperforms state of the art methods in terms of both accuracy and speed. To achieve this, we also offer a dataset with a more intuitive labeling scheme as compared to other benchmark datasets. Using our approach, we are able to obtain a lane segmentation accuracy of 99.87% running at 54.53 fps (average).

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