新闻聚合器
计算机科学
分割
特征(语言学)
特征提取
人工智能
目标检测
光学(聚焦)
频道(广播)
数据挖掘
语义学(计算机科学)
模式识别(心理学)
哲学
语言学
物理
光学
操作系统
计算机网络
程序设计语言
作者
Zengyu Qiu,Jing Zhao,Shiliang Sun
出处
期刊:IEEE Transactions on Intelligent Transportation Systems
[Institute of Electrical and Electronics Engineers]
日期:2022-12-01
卷期号:23 (12): 24263-24275
被引量:5
标识
DOI:10.1109/tits.2022.3195742
摘要
Lane detection differs from general object detection in that lane lines are usually long and narrow in the road image, and more attention to image features at different scales is required to reason about lane lines under occlusion, degradation, and bad weather. However, most existing semantic segmentation-based lane detection methods focus on solving the convolutional receptive field through aggregating information vertically and horizontally in the same feature map, which may ignore important information contained in multi-scale features. Besides, the high-level semantic information of whether the lane exists is not fully utilized, as they often add a module at the final stage of the network output to determine whether the lane exists, which is a dispensable for their network. Based on the above analysis, we design a novel lane detection network based on semantic segmentation which consists of a Multi-scale Feature Information Aggregator (MFIA) module and a Channel Attention (CA) module. Many experiments on the TRLane dataset, the generated Lane dataset, BDD100K dataset, TuSimple dataset, VIL-100 dataset and CULane dataset show that our approach can achieve the state-of-the-art performance (our code will be available at https://github.com/Cuibaby/MFIALane ). In addition, considering that different perceptual tasks in autonomous driving are able to share the feature extraction network, we also conduct the experiment for drivable area segmentation on BDD100K dataset. Our approach also achieves good results compared to many existing methods, showing that our proposed model is capable of simultaneously handling multiple perceptual tasks in autonomous driving scenarios.
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