Lane detection based on IBN deep neural network and attention. Issue 1 (31st December 2022)

• Record Type: Journal Article
• Authors: Song, Yue; Wang, Li-yong; Wang, Hao-dong; Li, Meng-lin
• Is Part Of: Connection science. Volume 34:Issue 1(2022)
• Page Start: 2671
• Page End: 2688
• Publication Date: 2022-12-31
• Subjects: Lane detection -- deep learning -- semantic segmentation -- IBN-Net -- attention module; Neural computers -- Periodicals; Artificial intelligence -- Periodicals; Cognitive science -- Periodicals; Connectionism -- Periodicals; 006.3
• DOI: 10.1080/09540091.2022.2139352
• Languages: English
• ISSNs: 0954-0091

Abstract

Abstract : Lane detection is an indispensable technology for environmental perception and is a function of autonomous vehicles. Although many researchers have applied deep learning to lane detection and achieved good results, their application scenarios were relatively simple. When a lane is blocked, lost, or met with other challenges, the accuracy of lane detection decreases tremendously. This paper proposes a novel semantic segmentation network for lane detection that includes IBN-Net, an attention module, and an encoder-decoder structure called IAED. IBN-Net improves the modelling and generalisation capability with little computational complexity. The attention module can better capture context information and improve the performance of the network. We evaluated the performance of the proposed method, which improved by 6.3% over ResNet34 on the TuSimple datasets. Tests on the CULane datasets showed that our method is at least 3% better than existing methods. Experimental results showed that the proposed method has strong robustness under complex conditions such as insufficient illumination and shadow occlusion.