Focus on Point: Parallel Multiscale Feature Aggregation for Lane Key Points Detection

Applied Sciences

Yanyan Zhang¹  Chao Zuo¹ 

[1] School of Electronics and Information Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China;

关键词: lane detection;    autonomous driving;    deep learning;   

DOI  :  10.3390/app12125975

来源: DOAJ

【 摘 要 】

Lane detection, as a basic environmental perception task, plays a significant role in the safety of automatic driving. Modern lane detection methods have obtained a better performance in most scenarios, but many are unsatisfactory in various scenarios, with a weak appearance (e.g., serious vehicle occlusion, dark shadows, ambiguous markings, etc.), and have issues in simplifying model predictions and flexibly detecting lanes of a non-fixed structure and number. In this work, we abstracted the lane lines as a series of discrete key points and proposed a lane detection method of parallel multi-scale feature aggregation based on key points, FPLane. The main task of FPLane is to focus on the precise location of key points in the global lanes and aggregate the global detection results into the local geometric modeling of lane lines by using the idea of association embedding. Furthermore, this work proposes the parallel Multi-scale Feature Aggregation network (MFANet) in FPLane integrating the context information of multi-scale feature mappings to take full advantage of the prior information of adjacent positions. In addition, MFANet incorporates the Double-headed Attention Feature Fusion Up-sampling module, which can facilitate the network to accurately recognize and detect objects under extreme scale variation. Finally, our method is tested on Tusimple and CULane lane detection datasets; the results show that the proposed method outperforms the current mainstream methods: the accuracy and F1-score of the model are 96.82% and 75.6%, respectively, and the real-time detection efficiency of the model can maintain 28 ms.

【 授权许可】

Unknown