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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 貝蘇章 | zh_TW |
| dc.contributor.advisor | Soo-Chang Pei | en |
| dc.contributor.author | 游家權 | zh_TW |
| dc.contributor.author | Jia-Quan Yu | en |
| dc.date.accessioned | 2023-08-08T16:17:26Z | - |
| dc.date.available | 2023-11-09 | - |
| dc.date.copyright | 2023-08-08 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-07-12 | - |
| dc.identifier.citation | [1] H. Abu Alhaija, S. K. Mustikovela, L. Mescheder, A. Geiger, and C. Rother. Augmented reality meets computer vision: Efficient data generation for urban driving scenes. International Journal of Computer Vision, 126:961–972, 2018.
[2] V. Badrinarayanan, A. Kendall, and R. Cipolla. Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence, 39(12):2481–2495, 2017. [3] A. Bansal, J. Singh, M. Verucchi, M. Caccamo, and L. Sha. Risk ranked recall: Collision safety metric for object detection systems in autonomous vehicles. In 2021 10th Mediterranean Conference on Embedded Computing (MECO), pages 1–4. IEEE, 2021. [4] W. Bao, B. Xu, and Z. Chen. Monofenet: Monocular 3d object detection with feature enhancement networks. IEEE Transactions on Image Processing, 29:2753–2765, 2019. [5] K. Bernardin and R. Stiefelhagen. Evaluating multiple object tracking performance: the clear mot metrics. EURASIP Journal on Image and Video Processing, 2008:1–10, 2008. [6] A. Bochkovskiy, C.-Y. Wang, and H.-Y. M. Liao. Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934, 2020. [7] D. Bolya, C. Zhou, F. Xiao, and Y. J. Lee. Yolact: Real-time instance segmentation. In Proceedings of the IEEE/CVF international conference on computer vision, pages 9157–9166, 2019. [8] G. Brazil and X. Liu. M3d-rpn: Monocular 3d region proposal network for object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9287–9296, 2019. [9] A. Ceccarelli and L. Montecchi. Safety-aware metrics for object detectors in autonomous driving. arXiv preprint arXiv:2203.02205, 2022. [10] L.-C. Chen, A. Hermans, G. Papandreou, F. Schroff, P. Wang, and H. Adam. Masklab: Instance segmentation by refining object detection with semantic and direction features. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4013–4022, 2018. [11] L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE transactions on pattern analysis and machine intelligence, 40(4):834–848, 2017. [12] L.-C. Chen, G. Papandreou, F. Schroff, and H. Adam. Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587, 2017. [13] L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam. Encoder-decoder with atrous separable convolution for semantic image segmentation. In Proceedings of the European conference on computer vision (ECCV), pages 801–818, 2018. [14] P. Chen, S. Liu, H. Zhao, and J. Jia. Gridmask data augmentation. arXiv preprint arXiv:2001.04086, 2020. [15] X. Chen, K. Kundu, Y. Zhu, A. G. Berneshawi, H. Ma, S. Fidler, and R. Urtasun. 3d object proposals for accurate object class detection. Advances in neural information processing systems, 28, 2015. [16] Y. Chen, L. Tai, K. Sun, and M. Li. Monopair: Monocular 3d object detection using pairwise spatial relationships. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12093–12102, 2020. [17] B. Cheng, M. D. Collins, Y. Zhu, T. Liu, T. S. Huang, H. Adam, and L.-C. Chen. Panoptic-deeplab: A simple, strong, and fast baseline for bottom-up panoptic segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 12475–12485, 2020. [18] M. Contributors. MMSegmentation: Openmmlab semantic segmentation toolbox and benchmark. https://github.com/open-mmlab/mmsegmentation, 2020. [19] J. Dai, H. Qi, Y. Xiong, Y. Li, G. Zhang, H. Hu, and Y. Wei. Deformable convolutional networks. In Proceedings of the IEEE international conference on computer vision, pages 764–773, 2017. [20] M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu, and P. Luo. Learning depth guided convolutions for monocular 3d object detection. In Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition workshops, pages 1000–1001, 2020. [21] C.-Y. Fu, W. Liu, A. Ranga, A. Tyagi, and A. C. Berg. Dssd: Deconvolutional single shot detector. arXiv preprint arXiv:1701.06659, 2017. [22] H. Fu, M. Gong, C. Wang, K. Batmanghelich, and D. Tao. Deep ordinal regression network for monocular depth estimation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2002–2011, 2018. [23] R. Girshick. Fast r-cnn. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), December 2015. [24] R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for ac curate object detection and semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2014. [25] K. He, G. Gkioxari, P. Dollar, and R. Girshick. Mask r-cnn. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), Oct 2017. [26] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016. [27] W. Kehl, F. Manhardt, F. Tombari, S. Ilic, and N. Navab. Ssd-6d: Making rgb-based 3d detection and 6d pose estimation great again. In Proceedings of the IEEE international conference on computer vision, pages 1521–1529, 2017. [28] A. Kirillov, R. Girshick, K. He, and P. Dollár. Panoptic feature pyramid networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6399–6408, 2019. [29] A. Kirillov, K. He, R. Girshick, C. Rother, and P. Dollár. Panoptic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9404–9413, 2019. [30] J. Ku, A. D. Pon, and S. L. Waslander. Monocular 3d object detection leveraging accurate proposals and shape reconstruction. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11867–11876, 2019. [31] A. H. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang, and O. Beijbom. Pointpillars: Fast encoders for object detection from point clouds. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 12697–12705, 2019. [32] P. Li, H. Zhao, P. Liu, and F. Cao. Rtm3d: Real-time monocular 3d detection from object keypoints for autonomous driving. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part III 16, pages 644–660. Springer, 2020. [33] Q. Lian, B. Ye, R. Xu, W. Yao, and T. Zhang. Exploring geometric consistency for monocular 3d object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1685–1694, 2022. [34] T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie. Feature pyramid networks for object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2117–2125, 2017. [35] T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár. Focal loss for dense object detection. In Proceedings of the IEEE international conference on computer vision, pages 2980–2988, 2017. [36] L. Liu, J. Lu, C. Xu, Q. Tian, and J. Zhou. Deep fitting degree scoring network for monocular 3d object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 1057–1066, 2019. [37] R. Liu, J. Lehman, P. Molino, F. Petroski Such, E. Frank, A. Sergeev, and J. Yosinski. An intriguing failing of convolutional neural networks and the coordconv solution. Advances in neural information processing systems, 31, 2018. [38] S. Liu, D. Huang, et al. Receptive field block net for accurate and fast object detection. In Proceedings of the European conference on computer vision (ECCV), pages 385–400, 2018. [39] S. Liu, L. Qi, H. Qin, J. Shi, and J. Jia. Path aggregation network for instance segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 8759–8768, 2018. [40] W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg. Ssd: Single shot multibox detector. In B. Leibe, J. Matas, N. Sebe, and M. Welling, editors, Computer Vision – ECCV 2016, pages 21–37, Cham, 2016. Springer International Publishing. [41] Y. Liu, Y. Yixuan, and M. Liu. Ground-aware monocular 3d object detection for autonomous driving. IEEE Robotics and Automation Letters, 6(2):919–926, 2021. [42] Z. Liu, Z. Wu, and R. Tóth. Smoke: Single-stage monocular 3d object detection via keypoint estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 996–997, 2020. [43] J. Long, E. Shelhamer, and T. Darrell. Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3431–3440, 2015. [44] F. Manhardt, W. Kehl, and A. Gaidon. Roi-10d: Monocular lifting of 2d detection to 6d pose and metric shape. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2069–2078, 2019. [45] A. Mousavian, D. Anguelov, J. Flynn, and J. Kosecka. 3d bounding box estimation using deep learning and geometry. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pages 7074–7082, 2017. [46] D. Park, R. Ambrus, V. Guizilini, J. Li, and A. Gaidon. Is pseudo-lidar needed for monocular 3d object detection? In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3142–3152, 2021. [47] C. R. Qi, H. Su, K. Mo, and L. J. Guibas. Pointnet: Deep learning on point sets for 3d classification and segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 652–660, 2017. [48] Z. Qin, J. Wang, and Y. Lu. Monogrnet: A geometric reasoning network for monocular 3d object localization. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 8851–8858, 2019. [49] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi. You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016. [50] J. Redmon and A. Farhadi. Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767, 2018. [51] S. Ren, K. He, R. Girshick, and J. Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. In C. Cortes, N. Lawrence, D. Lee, M. Sugiyama, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 28. Curran Associates, Inc., 2015. [52] T. Roddick, A. Kendall, and R. Cipolla. Orthographic feature transform for monocular 3d object detection. arXiv preprint arXiv:1811.08188, 2018. [53] O. Ronneberger, P. Fischer, and T. Brox. U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, pages 234–241. Springer, 2015. [54] J. Sang, Z. Wu, P. Guo, H. Hu, H. Xiang, Q. Zhang, and B. Cai. An improved yolov2 for vehicle detection. Sensors, 18(12):4272, 2018. [55] K. Santhakumar, B. R. Kiran, T. Gauthier, S. Yogamani, et al. Exploring 2d data augmentation for 3d monocular object detection. arXiv preprint arXiv:2104.10786, 2021. [56] A. Simonelli, S. R. Bulo, L. Porzi, M. López-Antequera, and P. Kontschieder. Disentangling monocular 3d object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1991–1999, 2019. [57] K. K. Singh, H. Yu, A. Sarmasi, G. Pradeep, and Y. J. Lee. Hide-and-seek: A data augmentation technique for weakly-supervised localization and beyond. arXiv preprint arXiv:1811.02545, 2018. [58] S. Srivastava, F. Jurie, and G. Sharma. Learning 2d to 3d lifting for object detection in 3d for autonomous vehicles. In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 4504–4511. IEEE, 2019. [59] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Van-houcke, and A. Rabinovich. Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1–9, 2015. [60] J. R. Uijlings, K. E. Van De Sande, T. Gevers, and A. W. Smeulders. Selective search for object recognition. International journal of computer vision, 104:154–171, 2013. [61] G. Volk, J. Gamerdinger, A. von Bernuth, and O. Bringmann. A comprehensive safety metric to evaluate perception in autonomous systems. In 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), pages 1–8. IEEE, 2020. [62] X. Wang, T. Kong, C. Shen, Y. Jiang, and L. Li. Solo: Segmenting objects by locations. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XVIII 16, pages 649–665. Springer, 2020. [63] X. Wang, R. Zhang, T. Kong, L. Li, and C. Shen. Solov2: Dynamic and fast instance segmentation. Advances in Neural information processing systems, 33:17721–17732, 2020. [64] M. Wolf, L. R. Douat, and M. Erz. Safety-aware metric for people detection. In 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), pages 2759–2765. IEEE, 2021. [65] Y. Wu, A. Kirillov, F. Massa, W.-Y. Lo, and R. Girshick. Detectron2. https://github.com/facebookresearch/detectron2, 2019. [66] S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He. Aggregated residual transformations for deep neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1492–1500, 2017. [67] Y. Xiong, R. Liao, H. Zhao, R. Hu, M. Bai, E. Yumer, and R. Urtasun. Upsnet: A unified panoptic segmentation network. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8818–8826, 2019. [68] Y. Yan, Y. Mao, and B. Li. Second: Sparsely embedded convolutional detection. Sensors, 18(10):3337, 2018. [69] T.-J. Yang, M. D. Collins, Y. Zhu, J.-J. Hwang, T. Liu, X. Zhang, V. Sze, G. Papandreou, and L.-C. Chen. Deeperlab: Single-shot image parser. arXiv preprint arXiv:1902.05093, 2019. [70] Y. You, Y. Wang, W.-L. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell, and K. Q. Weinberger. Pseudo-lidar++: Accurate depth for 3d object detection in autonomous driving. arXiv preprint arXiv:1906.06310, 2019. [71] F. Yu and V. Koltun. Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122, 2015. [72] S. Yun, D. Han, S. J. Oh, S. Chun, J. Choe, and Y. Yoo. Cutmix: Regularization strategy to train strong classifiers with localizable features. In Proceedings of the IEEE/CVF international conference on computer vision, pages 6023–6032, 2019. [73] R. Zhang, H. Qiu, T. Wang, X. Xu, Z. Guo, Y. Qiao, P. Gao, and H. Li. Monodetr: Depth-aware transformer for monocular 3d object detection. arXiv preprint arXiv:2203.13310, 2022. [74] Y. Zhang, J. Lu, and J. Zhou. Objects are different: Flexible monocular 3d object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3289–3298, 2021. [75] C. Zhou. Yolact++ Better Real-Time Instance Segmentation. University of California, Davis, 2020. [76] X. Zhou, D. Wang, and P. Krähenbühl. Objects as points. arXiv preprint arXiv:1904.07850, 2019. [77] Y. Zhou and O. Tuzel. Voxelnet: End-to-end learning for point cloud based 3d object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4490–4499, 2018. [78] X. Zhu, H. Hu, S. Lin, and J. Dai. Deformable convnets v2: More deformable, better results. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9308–9316, 2019. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88098 | - |
| dc.description.abstract | 影像辨識演算法在自駕車領域中扮演著關鍵的角色，它使得計算機能夠感知周圍環境並確保駕駛安全。本研究聚焦於自動駕駛場景中的物件檢測和影像分割任務的相關深度學習技術，針對三維物件辨識，本研究提出基於場景的數據擴增方法、錨點採樣技巧以及能夠改進特徵提取能力的相機視角卷積模組，本研究於KITTI3D數據集的測試中取得了顯著的23.9%平均準確度。此外，針對影像分割任務，我們提出了整合深度估計和全景分割的新任務並開發了Panoptic-DepthLab網絡，以實現分割和深度估計任務的共同訓練。本研究旨在提供自動駕駛領域中的影像識別演算法可能的改進方向，並設法提高物件檢測和影像分割的準確性和效率。 | zh_TW |
| dc.description.abstract | Image recognition plays a crucial role in autonomous driving, enabling computers to perceive the surrounding environment and ensure driving safety. In this thesis, we focus on both detection and segmentation tasks in driving scenarios, leveraging deep-learning techniques. Our work introduces novel approaches to enhance the performance of 3D object detection, including a scene-aware data augmentation method, a depth-aware anchor sampling technique, and a perspective-aware convolutional module that improves feature extraction capabilities. By combining these methods, we achieve a significant AP of 23.9% on the easy benchmark of the KITTI3D dataset. Furthermore, we propose a novel integration of depth estimation and panoptic segmentation on a color map. We also introduce the Panoptic-DepthLab network, which enables joint training of segmentation and depth estimation tasks. Our research aims to advance the field of image recognition in autonomous driving, improving the accuracy and efficiency of object detection and segmentation algorithms. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-08-08T16:17:26Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-08-08T16:17:26Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | Acknowledgements . . . . . . . . . . . . . . . . . . . . i
摘要 . . . . . . . . . . . . . . . . . . . . . . . . . . iii Abstract . . . . . . . . . . . . . . . . . . . . . . . . v Contents . . . . . . . . . . . . . . . . . . . . . . . . vii List of Figures . . . . . . . . . . . . . . . . . . . . xi List of Tables . . . . . . . . . . . . . . . . . . . . . xxv Chapter 1 Introduction . . . . . . . . . . . . . . . . . 1 Chapter 2 Object Detection . . . . . . . . . . . . . . . 5 2.1 Evaluation Metric - Average Precision . . . . . . . 5 2.2 Related Work . . . . . . . . . . . . . . . . . . . . 8 2.2.1 Two-stage Object Detector . . . . . . . . . . . . 8 2.2.2 Single-stage Object Detector . . . . . . . . . . . 10 2.3 Proposed Methods . . . . . . . . . . . . . . . . . . 15 2.3.1 Depth-aware Anchor Sampling . . . . . . . . . . . 17 2.3.2 K-means Anchor Dimension . . . . . . . . . . . . . 18 2.4 Experiment Result . . . . . . . . . . . . . . . . . 22 2.5 Summary . . . . . . . . . . . . . . . . . . . . . . 23 Chapter 3 Data Augmentation . . . . . . . . . . . . . . 25 3.1 Related Work . . . . . . . . . . . . . . . . . . . . 26 3.1.1 Data Augmentation for Object Detection . . . . . . 26 3.1.2 Data Augmentation for 3D Object Detection . . . . 28 3.2 Proposed Methods . . . . . . . . . . . . . . . . . . 32 3.2.1 Scene-aware Copy-paste Data Augmentation . . . . . 32 3.3 Experiment Result . . . . . . . . . . . . . . . . . 35 3.3.1 Quantitative Result . . . . . . . . . . . . . . . 35 3.3.2 Ablation Study . . . . . . . . . . . . . . . . . . 38 3.3.3 Qualitative Evaluation . . . . . . . . . . . . . . 40 3.4 Summary . . . . . . . . . . . . . . . . . . . . . . 42 Chapter 4 3D Object Detection in Driving Scene . . . . 47 4.1 Preliminary . . . . . . . . . . . . . . . . . . . . 47 4.1.1 Pinhole Camera Model . . . . . . . . . . . . . . . 48 4.2 Related Work . . . . . . . . . . . . . . . . . . . . 51 4.2.1 LiDAR-based 3D Object Detectors . . . . . . . . . 52 4.2.2 Monocular 3D Object Detectors . . . . . . . . . . 55 4.2.2.1 Two-stage detectors . . . . . . . . . . . . . . 55 4.2.2.2 Single-stage detectors . . . . . . . . . . . . . 59 4.2.2.3 Representation Transformation . . . . . . . . . 62 4.3 Proposed Methods . . . . . . . . . . . . . . . . . . 63 4.3.1 Backbone Improvement . . . . . . . . . . . . . . . 63 4.3.2 Perspective-aware Convolution . . . . . . . . . . 65 4.3.2.1 Convolutional Modules . . . . . . . . . . . . . 65 4.3.2.2 Perspective-aware Convolution Module . . . . . . 69 4.4 Experiment Result . . . . . . . . . . . . . . . . . 72 4.5 Summary . . . . . . . . . . . . . . . . . . . . . . 82 Chapter 5 Image Segmentation . . . . . . . . . . . . . 85 5.1 Evaluation Metric . . . . . . . . . . . . . . . . . 87 5.2 Related Works . . . . . . . . . . . . . . . . . . . 90 5.2.1 Semantic Segmentation . . . . . . . . . . . . . . 90 5.2.2 Instance Segmentation . . . . . . . . . . . . . . 95 5.2.3 Panoptic Segmentation . . . . . . . . . . . . . . 100 5.3 Proposed Method - Panoptic-DepthLab . . . . . . . . 103 5.4 Experiment Result . . . . . . . . . . . . . . . . . 107 5.4.1 Quantitative Result . . . . . . . . . . . . . . . 107 5.4.2 Qualitative Result . . . . . . . . . . . . . . . . 108 5.5 Summary . . . . . . . . . . . . . . . . . . . . . . 111 Chapter 6 Safety Metric in Driving Scenario . . . . . . 113 6.1 Related Work . . . . . . . . . . . . . . . . . . . . 115 6.2 Proposed Method - Safety-aware Average Precision . . 118 6.3 Experiment Result . . . . . . . . . . . . . . . . . 120 6.4 Summary . . . . . . . . . . . . . . . . . . . . . . 122 Chapter 7 Conclusion . . . . . . . . . . . . . . . . . 125 References . . . . . . . . . . . . . . . . . . . . . . 127 | - |
| dc.language.iso | en | - |
| dc.title | 駕駛場景中的影像辨識：三維物件辨識與影像分割 | zh_TW |
| dc.title | Image Recognition in Driving Scene: Monocular 3D Object Detection and Image Segmentation | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 丁建均;鍾國亮;杭學鳴;曾建誠 | zh_TW |
| dc.contributor.oralexamcommittee | Jian-Jiun Ding;Kuo-Liang Chung;Hsueh-Ming Hang;Chien-Cheng Tseng | en |
| dc.subject.keyword | 全景分割,物件辨識,安全性指標,駕駛場景,深度估計,三維物件辨識,語意分割,實例分割, | zh_TW |
| dc.subject.keyword | Panoptic Segmentation,Object Detection,Safety Metric,Driving Scene,Depth Estimation,3D Object Detection,Semantic Segmentation,Instance Segmentation, | en |
| dc.relation.page | 137 | - |
| dc.identifier.doi | 10.6342/NTU202301439 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2023-07-13 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 電信工程學研究所 | - |
| 顯示於系所單位： | 電信工程學研究所 | |
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