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姓名	曾昱瑋(Yu-Wei Tseng)  查詢紙本館藏	畢業系所	電機工程學系
論文名稱	基於具有座標注意力和邊緣檢測輔助之雙邊分割網路的實時語義分割任務 (Real-time Semantic Segmentation based on Bilateral Segmentation Network with Coordinate Attention and Edge Detection Support)
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檔案	[Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2025-8-1以後開放)
摘要(中)	語義分割任務在計算機視覺領域中一直是一個重要議題。近年來，卷積神經網路(Convolutional Neural Network)的作法也從比較早期的編碼器-解碼器(Encoder-Decoder)架構，演變至今各種架構都有人使用，對於語義分割任務來說，空間訊息和感受場(receptive field)是不可缺少的，為了使語義分割數方法幾乎都選擇在圖片解析度和低層次的細節訊息上做出妥協，這導致了準確性的大幅下降。在本文中，我們提出了一個基於雙邊分割網路(BiSeNet)的新架構，稱為BiSeNet V3。我們引入了一個新的特徵細化模組來優化特徵圖，以及一個特徵融合模組來有效結合特徵，引入了一個注意力機制來幫助模型提取上下文訊息，為了能更好的獲取特徵，我們還使用邊緣檢測來增強邊界的特徵。結合了這些方法，網路透過骨幹網路以及索伯算子(Sobel operator)提取特徵的同時，高解析度的特徵與低解析度的特徵透過本文提出的模組結合，在Cityscapes資料集上進行的大量實驗來驗證效果，我們提出的方法在分割精度和推理速度之間取得了優異的表現。具體來說，對於768 × 1536的輸入，BiSeNet V3在Cityscapes測試資料集上取得了79.0%的mIoU（Mean Intersection over Union），在NVIDIA GTX 1080Ti上的速度為93.8 FPS。對於720 × 960的輸入，BiSeNet V3在CamVid資料集上取得了76.6%的mIoU，在NVIDIA GTX 1080Ti上的速度為147.6 FPS。這樣的結果達到當前實時語義分割任務的state-of-the-art。
摘要(英)	Semantic segmentation has been an important issue in the field of computer vision. In recent years, the Convolutional Neural Network has evolved from the earlier Encoder-Decoder architecture to a variety of architectures. For the semantic segmentation task, spatial information and the receptive field are indispensable. For semantic segmentation to be practically applicable, it must have real-time inference speed. However, most of today’s methods almost choose to compromise the spatial resolution and low-level detail information, which leads to a significant decrease in accuracy. In this paper, we propose a new architecture based on Bilateral Segmentation Network (BiSeNet) called BiSeNet V3. It introduces a new feature refinement module to optimize the feature map and a feature fusion module to combine the features efficiently. An attention mechanism is introduced to assist the model in capturing contextual information. We also use edge detection to enhance features for boundaries. Combining these methods, the network extracts features through the backbone network and the Sobel operator while the high resolution features are combined with the low resolution features by the proposed module. The results are verified by extensive experiments on the Cityscapes dataset. Our proposed approach achieves an excellent performance between segmentation accuracy and inference speed. Specifically, for a 768×1536 input, BiSeNet V3 achieved 79.0% mIoU on the Cityscapes test set with a speed of 93.8 FPS on an NVIDIA GTX 1080Ti. For a 720×960 input, BiSeNet V3 achieved 76.6% mIoU on the CamVid dataset with a speed of 147.6 FPS on an NVIDIA GTX 1080Ti. The result outperforms other networks and archives the state-of-the-art of current real-time semantic segmentation task.
關鍵字(中)	★ 實時語義分割 ★ 深度學習	關鍵字(英)	★ Real-time Semantic Segmentation ★ Deep learning
論文目次	摘要 I ABSTRACT II 1.序論 1 1.1研究背景與動機 1 1.2論文架構 6 2.文獻探討 7 2.1高效骨幹網路 7 2.2傳統語義分割演算法 10 2.3實時語義分割演算法 13 2.4物件偵測演算法與系統實作 16 3.在實時語義分割任務上設計的雙邊分割網路 18 3.1設計動機與背景想法 18 3.2座標特徵細化模組(COORDINATE FEATURE REFINEMENT MODULE)以及座標特徵融合模組(COORDINATE FEATURE FUSION MODULE) 19 3.3邊緣檢測輔助方法 23 3.4雙邊分割網路架構設計 24 4.基於語義分割網路結合物件偵測網路的智慧資源回收物偵測系統 27 4.1設計動機與背景想法 27 4.2感興趣的區域模組(REGION OF INTEREST MODULE) 28 4.3智慧資源回收物偵測系統介紹 30 4.4語義分割網路結合物件偵測網路之架構設計 31 5.實驗結果 34 5.1資料集介紹 34 5.2實驗細節 36 5.3驗證指標 38 5.4比較結果—BISENET V3 39 5.5消融實驗 43 5.6比較結果—智慧資源回收物偵測系統之BILATRAL-YOLOV5 46 5.7討論 50 6.結論 51 參考文獻 52
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指導教授	蔡宗漢(Tsung-Han Tsai)	審核日期	2023-1-18
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