CNN based lane detection with instance segmentation in edge-cloud computing
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Journal of Cloud Computing: Advances, Systems and Applications
RESEARCH
Open Access
CNN based lane detection with instance segmentation in edge-cloud computing Wei Wang1 , Hui Lin2 and Junshu Wang3,4*
Abstract At present, the number of vehicle owners is increasing, and the cars with autonomous driving functions have attracted more and more attention. The lane detection combined with cloud computing can effectively solve the drawbacks of traditional lane detection relying on feature extraction and high definition, but it also faces the problem of excessive calculation. At the same time, cloud data processing combined with edge computing can effectively reduce the computing load of the central nodes. The traditional lane detection method is improved, and the current popular convolutional neural network (CNN) is used to build a dual model based on instance segmentation. In the image acquisition and processing processes, the distributed computing architecture provided by edge-cloud computing is used to improve data processing efficiency. The lane fitting process generates a variable matrix to achieve effective detection in the scenario of slope change, which improves the real-time performance of lane detection. The method proposed in this paper has achieved good recognition results for lanes in different scenarios, and the lane recognition efficiency is much better than other lane recognition models. Keywords: Double layer network, Lane line detection, Edge computing
Introduction With the advent of autonomous driving technology, people can largely get rid of the safety problems caused by daily manual driving. Therefore, self-driving cars are sought after by many automobile consumers. In recent years, many researchers from academic institutions and industries have engaged in autonomous driving technology and these researches have promoted the development of image processing and computer vision technology. As a key part of the automatic driving system, lane detection technology is meaningful. At present, the difficulty in lane detection is how to deal with lane detection accuracy and real-time issues at the same time, so we need to improve the accuracy and efficiency of lane recognition between traditional and neural network-based lane recognition methods. The traditional computer vision-based *Correspondence: [email protected] Key Laboratory for Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing, China 4 Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, China Full list of author information is available at the end of the article 3
lane detection technology is mainly based on image processing algorithms to extract the features of lane lines, reduce the image channels, perform gray processing on the original image, and then use Canny algorithm or Sobel algorithm to edge the grayed image, extract some features of the acquired image, and then perform lane line fitting after extracting the lane. Common lane fit
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