An Unmanned Lane Detection Algorithm Using Deep Learning and Ordered Test Sets Strategy
Authors: 
Shenwei Zhang, Xiaoyan Lin, Mingwei Zhang, Zhen Zhang, Yun Hou, Honglong Ning, Tian QiuAuthors Info & Claims
Shenwei Zhang, Xiaoyan Lin, Mingwei Zhang, Zhen Zhang, Yun Hou, Honglong Ning, Tian QiuAuthors Info & ClaimsAbstract
The traditional method of automatic lane detection is mostly based on Hough detection. However, this category of methods has low robustness and is vulnerable to interference. In order to improve the accuracy of lane detection, the presented paper compares and analyzes the end-to-end lane line detection network based on deep learning, including Unet-base and Deeplabv3+, in view of gradient explosion and slow running speed during model training, solutions are also given. Ordered test sets are used to speed up the training processing and validate the deep learning algorithm, in the case of different image resolutions, uses Unet-base and Deeplabv3+ to perform experiments respectively. Experiments show that under the same resolution, the Unet-base model with FCN network structure incorporating a better training strategy outperforms the Deeplabv3+ algorithm model that uses a classical ASSP module to solve the downsampling layer problem in terms of model generalization capability. And the MIOU of improved Unet-base is higher than Deeplabv3+. Therefore, compared to DeepLabv3+, the improved Unet-base model is more generalized.
References
[1]
TANG Zhiwei. Survey on Vision-based Autonomous Vehicles [J]. Manufacturing Automation, 2016, 38(8): 134-136.
[2]
Wu Lingling, Lin Zhixian, Guo Tailiang. Hough Transform lane Detection Based on Superposition Constraint [J]. Cable TV Technology,2019,2019(3):44-49.
[3]
SHI L J, YU Su. Lane detection method based on Hough transform under multiple constraints [J]. Computer Measurement and Control,2018,26(9):9-12.
[4]
Girshick R, Donahue J, Darrell T, Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation[C]. IEEE Conference on Computer Vision and Pattern Recognition, Columbus, USA, 2014: 580-587.
[5]
Redmon J, Divvala S, Girshick R, You Only Look Once: Unified, Real-time Object Detection[C]. IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 779-788. SSS
[6]
Qin Z, Wang H, Li X. Ultrafast structure-aware deep lane detection[C]//European Conference on Computer Vision. Springer, Cham, 2020: 276-291.
[7]
Neven D, De Brabandere B, Georgoulis S, Towards end-to-end lane detection: an instance segmentation approach[C]//2018 IEEE intelligent vehicles symposium (IV). IEEE, 2018: 286-291.
[8]
Li Yadi, Huang Haibo, Li Xiangpeng . Night lane detection Based on Canny operator and Hough Transform [J]. Science Technology and Engineering, 2016,16 (31): 234-237,242
[9]
Wang Xin, Liu Yuchao, Hai Dan. Lane detection based on Double ROI and variable interval scanning [J]. Journal of Command and Control, 2017, 3(2) :154-159.
[10]
Lu, Y., Chen, Y., Zhao, D., Chen, J. (2019). Graph-FCN for Image Semantic Segmentation. In: Lu, H., Tang, H., Wang, Z. (eds) Advances in Neural Networks – ISNN 2019. ISNN 2019. Lecture Notes in Computer Science(), vol 11554. Springer, Cham. https://doi.org/10.1007/978-3-030-22796-8_11
[11]
Biswas Sanad,Chambers Destini,Hairston W David,Bhattacharya Sylvia. Head pose classification for passenger with CNN[J]. Transportation Engineering,2023,11.
[12]
Kamal Shoaib,Shende Vaishali Gajendra,Swaroopa Korla,Bindhu Madhavi P.,Akram Patan Saleem,Pant Kumud,Patil Shantala Devi,Sahile Kibebe. FCN Network-Based Weed and Crop Segmentation for IoT-Aided Agriculture Applications[J]. Wireless Communications and Mobile Computing,2022,2022.
[13]
Liu J, Wang Z, Cheng K. An improved algorithm for semantic segmentation of remote sensing images based on DeepLabv3+[C]//Proceedings of the 5th international conference on communication and information processing. 2019: 124-128.
[14]
Li Meimei, Hu Chunhai, Long Ping, Liu Shaonan. Lane Detection Algorithm Based on MultiRes+UNet Network [J]. Journal of Electronic Measurement and Instrument,2020,34(9):1-6 [10] KIM J, LEE M.Robust lane detection based onconvolutional neural network and random sampleconsensus [C]. ICONIP, 2014:454-461.
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Published In
March 2023
598 pages
ISBN:9781450399449
DOI:10.1145/3590003
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Association for Computing Machinery
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Published: 29 May 2023
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CACML 2023
CACML 2023: 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning
March 17 - 19, 2023
Shanghai, China
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CACML '23 Paper Acceptance Rate 93 of 241 submissions, 39%;
Overall Acceptance Rate 93 of 241 submissions, 39%
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