References
[1]. W. Xu, B. Li, S. Liu, and W. Qiu, “Real-time object detection and semantic segmentation for autonomous driving,” Feb. 2018, p. 44. doi: 10.1117/12.2288713.
[2]. A. Voulodimos, N. Doulamis, A. Doulamis, and E. Protopapadakis, “Deep Learning for Computer Vision: A Brief Review,” Computational Intelligence and Neuroscience, vol. 2018, pp. 1–13, 2018, doi: 10.1155/2018/7068349.
[3]. S. Zhou and W. Song, “Deep learning-based roadway crack classification using laser-scanned range images: A comparative study on hyperparameter selection,” Automation in Construction, vol. 114, p. 103171, Jun. 2020, doi: 10.1016/j.autcon.2020.103171.
[4]. J. C. P. Cheng and M. Wang, “Automated detection of sewer pipe defects in closed-circuit television images using deep learning techniques,” Autom. Constr., vol. 95, pp. 155–171, Nov. 2018, doi: 10.1016/j.autcon.2018.08.006.
[5]. M. Wang and J. C. P. Cheng, “A unified convolutional neural network integrated with conditional random field for pipe defect segmentation,” Computer-Aided Civil and Infrastructure Engineering, Jul. 2019, doi: 10.1111/mice.12481.
[6]. M. Dildar et al., “Skin Cancer Detection: A Review Using Deep Learning Techniques,” Int. J. Environ. Res. Public. Health, vol. 18, no. 10, p. 5479, May 2021, doi: 10.3390/ijerph18105479.
[7]. M. ur Rehman, S. H. Khan, S. M. Danish Rizvi, Z. Abbas, and A. Zafar, “Classification of Skin Lesion by Interference of Segmentation and Convolotion Neural Network,” in 2018 2nd International Conference on Engineering Innovation (ICEI), Jul. 2018, pp. 81–85. doi: 10.1109/ICEI18.2018.8448814.
[8]. L. Chen, W. Zhan, W. Tian, Y. He, and Q. Zou, “Deep Integration: A Multi-Label Architecture for Road Scene Recognition,” IEEE Trans. Image Process., vol. 28, no. 10, pp. 4883–4898, 2019, doi: 10.1109/TIP.2019.2913079.
[9]. G. Li et al., “ML-ANet: A Transfer Learning Approach Using Adaptation Network for Multi-label Image Classification in Autonomous Driving,” Chin. J. Mech. Eng. Ji Xie Gong Cheng Xue Bao Engl. Ed, vol. 34, no. 1, Dec. 2021, doi: 10.1186/s10033-021-00598-9.
[10]. G. Li, Y. Yang, and X. Qu, “Deep Learning Approaches on Pedestrian Detection in Hazy Weather,” IEEE Trans. Ind. Electron., vol. 67, no. 10, pp. 8889–8899, 2020, doi: 10.1109/TIE.2019.2945295.
[11]. J. Redmon and A. Farhadi, “YOLO9000: Better, Faster, Stronger,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jul. 2017, pp. 6517–6525. doi: 10.1109/CVPR.2017.690.
[12]. M. Hnewa and H. Radha, “Object Detection Under Rainy Conditions for Autonomous Vehicles: A Review of State-of-the-Art and Emerging Techniques,” IEEE Signal Process. Mag., vol. 38, no. 1, pp. 53–67, 2021, doi: 10.1109/MSP.2020.2984801.
[13]. M.-Y. Liu, T. Breuel, and J. Kautz, “Unsupervised Image-to-Image Translation Networks.” arXiv, Jul. 22, 2018. doi: 10.48550/arXiv.1703.00848.
[14]. Y. Chen, W. Li, C. Sakaridis, D. Dai, and L. Van Gool, “Domain Adaptive Faster R-CNN for Object Detection in the Wild,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Jun. 2018, pp. 3339–3348. doi: 10.1109/CVPR.2018.00352.
[15]. N. A. M. Mai, P. Duthon, L. Khoudour, A. Crouzil, and S. A. Velastin, “Sparse LiDAR and Stereo Fusion (SLS-Fusion) for Depth Estimationand 3D Object Detection.” arXiv, May 28, 2021. doi: 10.48550/arXiv.2103.03977.
[16]. G. Li, Y. Yang, X. Qu, D. Cao, and K. Li, “A deep learning based image enhancement approach for autonomous driving at night,” Knowledge-Based Systems, vol. 213, p. 106617, Feb. 2021, doi: 10.1016/j.knosys.2020.106617.
[17]. X. Cheng, P. Wang, and R. Yang, “Learning Depth with Convolutional Spatial Propagation Network,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 42, no. 10, pp. 2361–2379, 2020, doi: 10.1109/TPAMI.2019.2947374.
[18]. X. Ma, Z. Wang, H. Li, P. Zhang, W. Ouyang, and X. Fan, “Accurate Monocular 3D Object Detection via Color-Embedded 3D Reconstruction for Autonomous Driving,” in 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 6850–6859. doi: 10.1109/ICCV.2019.00695.
[19]. P. Radecki, M. Campbell, and K. Matzen, “All Weather Perception: Joint Data Association, Tracking, and Classification for Autonomous Ground Vehicles.” arXiv, May 07, 2016. doi: 10.48550/arXiv.1605.02196.
[20]. H. Gao, B. Cheng, J. Wang, K. Li, J. Zhao, and D. Li, “Object Classification Using CNN-Based Fusion of Vision and LIDAR in Autonomous Vehicle Environment,” IEEE Trans. Ind. Inform., vol. 14, no. 9, pp. 4224–4231, Sep. 2018, doi: 10.1109/TII.2018.2822828.
[21]. W. Boyuan and W. Muqing, “Study on Pedestrian Detection Based on an Improved YOLOv4 Algorithm,” in 2020 IEEE 6th International Conference on Computer and Communications (ICCC), 2020, pp. 1198–1202. doi: 10.1109/ICCC51575.2020.9344983.
[22]. A. Hechri and A. Mtibba, “Two-Stage Traffic Sign Detection and Recognition Based on SVM and Convolutional Neural Networks,” IET Image Processing, Dec. 2019, doi: 10.1049/iet-ipr.2019.0634.
[23]. J. Müller and K. Dietmayer, "Detecting Traffic Lights by Single Shot Detection," 2018 21st International Conference on Intelligent Transportation Systems (ITSC), 2018, pp. 266-273, doi: 10.1109/ITSC.2018.8569683.
[24]. X.-Y. Ye, D.-S. Hong, H.-H. Chen, P.-Y. Hsiao, and L.-C. Fu, “A two-stage real-time YOLOv2-based road marking detector with lightweight spatial transformation-invariant classification,” Image Vis. Comput., vol. 102, p. 103978, Oct. 2020, doi: 10.1016/j.imavis.2020.103978.
[25]. S. Papadopoulos, I. Mademlis and I. Pitas, "Neural vision-based semantic 3D world modeling," 2021 IEEE Winter Conference on Applications of Computer Vision Workshops (WACVW), 2021, pp. 181-190, doi: 10.1109/WACVW52041.2021.00024.
[26]. A. Kherraki, M. Maqbool, and R. El Ouazzani, “Traffic Scene Semantic Segmentation by Using Several Deep Convolutional Neural Networks,” in 2021 3rd IEEE Middle East and North Africa COMMunications Conference (MENACOMM), 2021, pp. 1–6. doi: 10.1109/MENACOMM50742.2021.9678270.
[27]. S. Papadopoulos, I. Mademlis and I. Pitas, "Semantic Image Segmentation Guided By Scene Geometry," 2021 IEEE International Conference on Autonomous Systems (ICAS), 2021, pp. 1-5, doi: 10.1109/ICAS49788.2021.9551117.
[28]. M. S. S. Mahecha, O. J. S. Parra, and J. B. Velandia, “Design of a System for Melanoma Detection Through the Processing of Clinical Images Using Artificial Neural Networks,” Lecture Notes in Computer Science, pp. 605–616, 2018, doi: 10.1007/978-3-030-02131-3_53.
Cite this article
Zhang,T. (2023). A review of the application of CNN-based computer vision in auto-driving. Applied and Computational Engineering,5,69-74.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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References
[1]. W. Xu, B. Li, S. Liu, and W. Qiu, “Real-time object detection and semantic segmentation for autonomous driving,” Feb. 2018, p. 44. doi: 10.1117/12.2288713.
[2]. A. Voulodimos, N. Doulamis, A. Doulamis, and E. Protopapadakis, “Deep Learning for Computer Vision: A Brief Review,” Computational Intelligence and Neuroscience, vol. 2018, pp. 1–13, 2018, doi: 10.1155/2018/7068349.
[3]. S. Zhou and W. Song, “Deep learning-based roadway crack classification using laser-scanned range images: A comparative study on hyperparameter selection,” Automation in Construction, vol. 114, p. 103171, Jun. 2020, doi: 10.1016/j.autcon.2020.103171.
[4]. J. C. P. Cheng and M. Wang, “Automated detection of sewer pipe defects in closed-circuit television images using deep learning techniques,” Autom. Constr., vol. 95, pp. 155–171, Nov. 2018, doi: 10.1016/j.autcon.2018.08.006.
[5]. M. Wang and J. C. P. Cheng, “A unified convolutional neural network integrated with conditional random field for pipe defect segmentation,” Computer-Aided Civil and Infrastructure Engineering, Jul. 2019, doi: 10.1111/mice.12481.
[6]. M. Dildar et al., “Skin Cancer Detection: A Review Using Deep Learning Techniques,” Int. J. Environ. Res. Public. Health, vol. 18, no. 10, p. 5479, May 2021, doi: 10.3390/ijerph18105479.
[7]. M. ur Rehman, S. H. Khan, S. M. Danish Rizvi, Z. Abbas, and A. Zafar, “Classification of Skin Lesion by Interference of Segmentation and Convolotion Neural Network,” in 2018 2nd International Conference on Engineering Innovation (ICEI), Jul. 2018, pp. 81–85. doi: 10.1109/ICEI18.2018.8448814.
[8]. L. Chen, W. Zhan, W. Tian, Y. He, and Q. Zou, “Deep Integration: A Multi-Label Architecture for Road Scene Recognition,” IEEE Trans. Image Process., vol. 28, no. 10, pp. 4883–4898, 2019, doi: 10.1109/TIP.2019.2913079.
[9]. G. Li et al., “ML-ANet: A Transfer Learning Approach Using Adaptation Network for Multi-label Image Classification in Autonomous Driving,” Chin. J. Mech. Eng. Ji Xie Gong Cheng Xue Bao Engl. Ed, vol. 34, no. 1, Dec. 2021, doi: 10.1186/s10033-021-00598-9.
[10]. G. Li, Y. Yang, and X. Qu, “Deep Learning Approaches on Pedestrian Detection in Hazy Weather,” IEEE Trans. Ind. Electron., vol. 67, no. 10, pp. 8889–8899, 2020, doi: 10.1109/TIE.2019.2945295.
[11]. J. Redmon and A. Farhadi, “YOLO9000: Better, Faster, Stronger,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jul. 2017, pp. 6517–6525. doi: 10.1109/CVPR.2017.690.
[12]. M. Hnewa and H. Radha, “Object Detection Under Rainy Conditions for Autonomous Vehicles: A Review of State-of-the-Art and Emerging Techniques,” IEEE Signal Process. Mag., vol. 38, no. 1, pp. 53–67, 2021, doi: 10.1109/MSP.2020.2984801.
[13]. M.-Y. Liu, T. Breuel, and J. Kautz, “Unsupervised Image-to-Image Translation Networks.” arXiv, Jul. 22, 2018. doi: 10.48550/arXiv.1703.00848.
[14]. Y. Chen, W. Li, C. Sakaridis, D. Dai, and L. Van Gool, “Domain Adaptive Faster R-CNN for Object Detection in the Wild,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Jun. 2018, pp. 3339–3348. doi: 10.1109/CVPR.2018.00352.
[15]. N. A. M. Mai, P. Duthon, L. Khoudour, A. Crouzil, and S. A. Velastin, “Sparse LiDAR and Stereo Fusion (SLS-Fusion) for Depth Estimationand 3D Object Detection.” arXiv, May 28, 2021. doi: 10.48550/arXiv.2103.03977.
[16]. G. Li, Y. Yang, X. Qu, D. Cao, and K. Li, “A deep learning based image enhancement approach for autonomous driving at night,” Knowledge-Based Systems, vol. 213, p. 106617, Feb. 2021, doi: 10.1016/j.knosys.2020.106617.
[17]. X. Cheng, P. Wang, and R. Yang, “Learning Depth with Convolutional Spatial Propagation Network,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 42, no. 10, pp. 2361–2379, 2020, doi: 10.1109/TPAMI.2019.2947374.
[18]. X. Ma, Z. Wang, H. Li, P. Zhang, W. Ouyang, and X. Fan, “Accurate Monocular 3D Object Detection via Color-Embedded 3D Reconstruction for Autonomous Driving,” in 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 6850–6859. doi: 10.1109/ICCV.2019.00695.
[19]. P. Radecki, M. Campbell, and K. Matzen, “All Weather Perception: Joint Data Association, Tracking, and Classification for Autonomous Ground Vehicles.” arXiv, May 07, 2016. doi: 10.48550/arXiv.1605.02196.
[20]. H. Gao, B. Cheng, J. Wang, K. Li, J. Zhao, and D. Li, “Object Classification Using CNN-Based Fusion of Vision and LIDAR in Autonomous Vehicle Environment,” IEEE Trans. Ind. Inform., vol. 14, no. 9, pp. 4224–4231, Sep. 2018, doi: 10.1109/TII.2018.2822828.
[21]. W. Boyuan and W. Muqing, “Study on Pedestrian Detection Based on an Improved YOLOv4 Algorithm,” in 2020 IEEE 6th International Conference on Computer and Communications (ICCC), 2020, pp. 1198–1202. doi: 10.1109/ICCC51575.2020.9344983.
[22]. A. Hechri and A. Mtibba, “Two-Stage Traffic Sign Detection and Recognition Based on SVM and Convolutional Neural Networks,” IET Image Processing, Dec. 2019, doi: 10.1049/iet-ipr.2019.0634.
[23]. J. Müller and K. Dietmayer, "Detecting Traffic Lights by Single Shot Detection," 2018 21st International Conference on Intelligent Transportation Systems (ITSC), 2018, pp. 266-273, doi: 10.1109/ITSC.2018.8569683.
[24]. X.-Y. Ye, D.-S. Hong, H.-H. Chen, P.-Y. Hsiao, and L.-C. Fu, “A two-stage real-time YOLOv2-based road marking detector with lightweight spatial transformation-invariant classification,” Image Vis. Comput., vol. 102, p. 103978, Oct. 2020, doi: 10.1016/j.imavis.2020.103978.
[25]. S. Papadopoulos, I. Mademlis and I. Pitas, "Neural vision-based semantic 3D world modeling," 2021 IEEE Winter Conference on Applications of Computer Vision Workshops (WACVW), 2021, pp. 181-190, doi: 10.1109/WACVW52041.2021.00024.
[26]. A. Kherraki, M. Maqbool, and R. El Ouazzani, “Traffic Scene Semantic Segmentation by Using Several Deep Convolutional Neural Networks,” in 2021 3rd IEEE Middle East and North Africa COMMunications Conference (MENACOMM), 2021, pp. 1–6. doi: 10.1109/MENACOMM50742.2021.9678270.
[27]. S. Papadopoulos, I. Mademlis and I. Pitas, "Semantic Image Segmentation Guided By Scene Geometry," 2021 IEEE International Conference on Autonomous Systems (ICAS), 2021, pp. 1-5, doi: 10.1109/ICAS49788.2021.9551117.
[28]. M. S. S. Mahecha, O. J. S. Parra, and J. B. Velandia, “Design of a System for Melanoma Detection Through the Processing of Clinical Images Using Artificial Neural Networks,” Lecture Notes in Computer Science, pp. 605–616, 2018, doi: 10.1007/978-3-030-02131-3_53.