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https://ah.lib.nccu.edu.tw/handle/140.119/137677| 題名: | 水下顯著物目標檢測 Underwater Salient Object Detection |
作者: | 林祐丞 Lin, Yu-Cheng |
貢獻者: | 彭彥璁 Peng, Yan-Tsung 林祐丞 Lin, Yu-Cheng |
關鍵詞: | 顯著物偵測 資料擴增 深度學習 Underwater salient object detection Data augmentation Deep learning |
日期: | 2021 | 上傳時間: | 1-十一月-2021 | 摘要: | 顯著物偵測(SOD)在深度學習架構下已達到相當先進的成果。然而既有的研究大部分都專注在陸上場景，水下場景的顯著物偵測仍有待發展。在這篇論文中，我們蒐集並標註一水下顯著物資料集，用以驗證我們提出的模型方法。本論文中提出二種方法提昇顯著物偵測準確度。第一，我們先嘗試利用了水下影像模糊特性，幫助深度網路學習顯著物偵測。首先，我們會從原圖計算生成模糊圖，並與原圖一起輸入模型抽取特徵並融合，藉以提昇顯著物偵測準確度。第二，我們提出基於模糊圖對原圖增益作調整的一種資料擴增的方法。實驗結果顯示在最新顯著物偵測模型上，使用這兩種方法，皆可有效提昇效能。而提出的資料擴增方法的成效，比第一種方法更為有效。 Salient object detection (SOD) has achieved state-of-the-art performance with the help of deep networks. However, most of the works focus on terrestrial scenes, and underwater scenes for SOD are still unexplored. In this work, we propose two practical approaches to boost the performance of underwater SOD. First, we utilize image blurriness to enable a more accurate SOD prediction. The blurriness map is calculated based on the input image, fed into the model with the input, and fused with the input image to produce the saliency map. Next, we propose a data augmentation method called FocusAugment for underwater SOD, which adjusts the image intensity based on the blurriness map. We can modify images by highlighting less blurred regions or enlarging the difference of pixels based on the blurriness maps. We test underwater SOD by the proposed dataset collected and annotated by ourselves for evaluation. The experimental results show that both of our approaches work; moreover, the presented FocusAugment works better than the blurriness-guided SOD model. |
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資料來源: | http://thesis.lib.nccu.edu.tw/record/#G0108753209 | 資料類型: | thesis |
| Appears in Collections: | 學位論文 |
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