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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 傅立成(Li-Chen Fu) | |
| dc.contributor.author | Chia-Hung Wang | en |
| dc.contributor.author | 王嘉鴻 | zh_TW |
| dc.date.accessioned | 2022-11-25T06:33:24Z | - |
| dc.date.copyright | 2021-11-09 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-08-19 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/82189 | - |
| dc.description.abstract | 傳統上，汽車工業為硬體導向產業，近年來，深度學習爆炸性的成長，將電腦視覺推向嶄新的境界。因此，自動駕駛的可能性不再是不可觸及的夢想。其中自動駕駛的第一環節即是感知環境，常見的感測器如光達、攝影機等，利用各種感測器的提供的資訊偵測出道路上的物件。然而各種感測器皆有其優勢及劣勢，因此多感測器融合是一個有效提升偵測結果的方式。本論文旨在提出一種深度學習方法，整合光達點雲與攝影機影像特徵並且偵測出三維道路物件。 本論文提出一種創新且基於融合的三維物件偵測網路，稱作「體素與像素融合網路」。位於其中的「體素與像素融合層」包含了「參數特徵生成」、「基於參數的權重調整」和「體素與像素融合」，共三個模組，能根據幾何關係雙向地融合一對體素與像素的特徵。此外，我們提出的「體素與像素參數」能考慮每一對體素與像素的特性，並加強融合效果。 本研究使用知名的KITTI資料集訓練模型，並於官方未公開標註的測試集評估。實驗結果顯示本方法在多類別與多難度的三維物件偵測平均精確度達到65.99%。值得注意的是，我們的方法在KITTI排行榜中具有挑戰性的行人類別位居第一名。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-25T06:33:24Z (GMT). No. of bitstreams: 1 U0001-2507202117231500.pdf: 4116803 bytes, checksum: 61c57782e835ba55b520490478e08ffa (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 摘要 i Abstract iii Table of Contents v List of Figures ix List of Tables xii Chapter 1 Introduction 1 1.1 Background 1 1.2 Motivation 7 1.3 Challenge 8 1.4 Objective 9 1.5 Related Work 10 1.5.1 Camera-Based 3D Object Detector 10 1.5.2 LiDAR-Based 3D Object Detector 11 1.5.3 Fusion-Based 3D Object Detector 12 1.6 Contribution 13 1.7 Thesis Organization 14 Chapter 2 Preliminaries 17 2.1 Artificial Neural Network 17 2.1.1 Fully Connected Layer 18 2.1.2 Normalization Layer 19 2.1.3 Activation Function 20 2.1.4 Backpropagation 23 2.1.5 Stochastic Gradient Descent Optimizer 24 2.1.6 Adam Optimizer 24 2.2 Convolutional Neural Network 25 2.2.1 Convolutional Layer 25 2.2.2 Pooling Layer 28 2.2.3 Normalization Layer 31 2.2.4 Fully Connected Layer 31 2.3 Image Classification Framework 32 2.3.1 AlexNet 32 2.3.2 VGGNet 33 2.3.3 ResNet 33 2.3.4 ResNeXt 34 2.4 Object Detection Framework 35 2.4.1 Faster R-CNN 36 2.4.2 Cascade R-CNN 36 2.4.3 PV-RCNN 37 Chapter 3 Voxel-Pixel Fusion Network 41 3.1 Problem Formulation 41 3.2 Architecture Overview 43 3.3 Pre-processing 44 3.4 Backbone Network 47 3.4.1 Backbone Network in LiDAR Stream 47 3.4.2 Backbone Network in Camera Stream 49 3.5 Voxel-Pixel Fusion Layer 50 3.5.1 Parameter Feature Generating Module 52 3.5.2 Parameter-Based Weighting Module 56 3.5.3 Voxel-Pixel Fusion Module 58 3.6 Region Proposal Network 61 3.6.1 Region Proposal Network in LiDAR Stream 62 3.6.2 Region Proposal Network in Camera Stream 63 3.7 Detection Network 63 3.7.1 Detection Network in LiDAR Stream 64 3.7.2 Detection Network in Camera Stream 65 3.8 Loss Function 66 Chapter 4 Experiment 71 4.1 KITTI Vision Benchmark 71 4.2 Evaluation Metric 74 4.2.1 Intersection over Union 74 4.2.2 Mean Average Precision 75 4.3 Implementation Detail 78 4.3.1 Experimental Platform 78 4.3.2 Training Detail 79 4.4 Ablation Study 80 4.4.1 Effect of Modules within VPF Layer 81 4.4.2 Effect of Arrangement of VPF Layers 84 4.5 Quantitative Result 87 4.5.1 3D Object Detection 88 4.5.2 BEV Object Detection 92 4.6 Qualitative Result 96 4.6.1 Visualization 97 4.6.2 Precision-Recall Curve 99 4.7 Discussion 102 Chapter 5 Conclusion 107 Reference 109 | |
| dc.language.iso | en | |
| dc.subject | 電腦視覺 | zh_TW |
| dc.subject | 感知融合 | zh_TW |
| dc.subject | 自動駕駛 | zh_TW |
| dc.subject | 三維物件偵測 | zh_TW |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | 人工智慧 | zh_TW |
| dc.subject | deep learning | en |
| dc.subject | artificial intelligence | en |
| dc.subject | autonomous driving | en |
| dc.subject | computer vision | en |
| dc.subject | sensor fusion | en |
| dc.subject | 3D object detection | en |
| dc.title | 應用於三維道路物件偵測之體素與像素融合網路 | zh_TW |
| dc.title | Voxel-Pixel Fusion Network for 3D On-road Object Detection | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 蕭培墉(Pei-Yung Hsiao) | |
| dc.contributor.oralexamcommittee | 傅楸善(Hsin-Tsai Liu),方瓊瑤(Chih-Yang Tseng),林忠緯 | |
| dc.subject.keyword | 感知融合,三維物件偵測,自動駕駛,電腦視覺,深度學習,人工智慧, | zh_TW |
| dc.subject.keyword | sensor fusion,3D object detection,autonomous driving,computer vision,deep learning,artificial intelligence, | en |
| dc.relation.page | 121 | |
| dc.identifier.doi | 10.6342/NTU202101725 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2021-08-20 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| dc.date.embargo-lift | 2026-08-01 | - |
| 顯示於系所單位： | 資訊工程學系 | |
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| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| U0001-2507202117231500.pdf 未授權公開取用 | 4.02 MB | Adobe PDF | 檢視/開啟 |
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