车道线检测——Efficient Road Lane Marking Detection With Deep Learning,程序员大本营,技术文章内容聚合第一站。
Road lane lines detectionRoad segmentationDriving assistanceOne of the most important challenges for Autonomous Driving and Driving Assistance systems is the detection of the road to perform or monitor navigation. Many works can be found in the literature to perform road and lane detection, using ...
Addressing the issues of low detection accuracy inherent in traditional image processing methods and poor real-time performance associated with deep learning-based methods, Huu et al.14 proposed a dual-lane detection algorithm based on computer vision. This involved the utilization of a pre-trained ...
Although the aforementioned approaches provide a promising performance of lane and road marking detection using deep learning, the problem of detection under poor conditions is still not solved. In this paper, we propose a network that performs well in any situation including bad weather and low ...
Later in 2021, we published another work of image-based pixel-level road crack detection using deep learning [28]. Comparing our experiences from these two previous works, the latter work in 2021 was superior in terms of accurate results that were more robust to unpredictable visual artifacts ...
The results of the pipeline are quantied by rst measuring its accuracy in the classication of road signs, second measuring its ability to gather the information about the road (lane analysis and 2 vehicle detection) thirdly by performing the time bench-marking....
Ultra Fast Structure-aware Deep Lane Detection (ECCV 2020) cnn pytorch lane-finding autonomous-driving autonomous-vehicles lane-detection lane-detector road-detection Updated Dec 14, 2022 Python hlwang1124 / SNE-RoadSeg Star 316 Code Issues Pull requests SNE-RoadSeg for Freespace Detection in ...
Detecting roads in automatic driving environments poses a challenge due to issues such as boundary fuzziness, occlusion, and glare from light. We believe that two factors are instrumental in addressing these challenges and enhancing detection performance
The overall pipeline is lightweight and could easily be applied to real-time system for lane detection using visual data. The architecture uses a series of traditional computer vision techniques. The downside of such an approach being that each stage of the image processing pipeline has to be ...
lead to more accurate detection of the dependencies. For example, Zhang et al. (2021a,b) use a matrix that includes physical proximity information fused with cosine similarity and graph betweenness metrics. Furthermore, in Zhang et al. (2020a,b) a Structure Learning Convolution (SLC) framework...