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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-Nov-2021 12:00:43 (UTC+8) 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.參考文獻 [1] D. V. Ruiz, B. A. Krinski, and E. Todt, “Ida: Improved data augmentation applied to salient object detection,” in 2020 33rd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI). IEEE, 2020.[2] Y. Pang, X. Zhao, L. Zhang, and H. Lu, “Multiscale interactive network for salient object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020.[3] D. Zhang, D. Meng, L. Zhao, and J. Han, “Bridging saliency detection to weakly supervised object detection based on selfpaced curriculum learning,” arXiv preprint arXiv:1703.01290, 2017.[4] Z. Ren, S. Gao, L.T. Chia, and I. W.H. Tsang, “Regionbased saliency detection and its application in object recognition,” IEEE Transactions on Circuits and Systems for Video Technology, 2013.[5] S. P. Bharati, S. Nandi, Y. Wu, Y. Sui, and G. Wang, “Fast and robust object tracking with adaptive detection,” in 2016 IEEE 28th international conference on tools with artificial intelligence (ICTAI). IEEE, 2016.[6] H. Lee and D. Kim, “Salient regionbased online object tracking,” in 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2018. 30[7] M. Cornia, L. Baraldi, G. Serra, and R. Cucchiara, “Paying more attention to saliency: Image captioning with saliency and context attention,” ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 2018.[8] H. Hadizadeh and I. V. Bajić, “Saliencyaware video compression,” IEEE Transactions on Image Processing, vol. 23, no. 1, pp. 19–33, 2013.[9] Q.G. Ji, Z.D. Fang, Z.H. Xie, and Z.M. Lu, “Video abstraction based on the visual attention model and online clustering,” Signal Processing: Image Communication, vol. 28, no. 3, pp. 241–253, 2013.[10] Y. Ban and K. Lee, “Reenrichment learning: Metadata saliency for the evolutive personalization of a recommender system,” Applied Sciences, vol. 11, no. 4, p. 1733, 2021.[11] J. Zhang, X. Yu, A. Li, P. Song, B. Liu, and Y. Dai, “Weaklysupervised salient object detection via scribble annotations,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 12 546–12 555.[12] P. Siva, C. Russell, T. Xiang, and L. Agapito, “Looking beyond the image: Unsupervised learning for object saliency and detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2013, pp. 3238–3245.[13] L. Wang, H. Lu, Y. Wang, M. Feng, D. Wang, B. Yin, and X. Ruan, “Learning to detect salient objects with imagelevel supervision,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 136–145.[14] D.P. Fan, T. Li, Z. Lin, G.P. Ji, D. Zhang, M.M. Cheng, H. Fu, and J. Shen, “Rethinking cosalient object detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021.[15] M. J. Islam, R. Wang, K. de Langis, and J. Sattar, “Svam: Saliencyguided visual attention modeling by autonomous underwater robots,” arXiv preprint arXiv:2011.06252, 2020.[16] M. J. Islam, P. Luo, and J. Sattar, “Simultaneous enhancement and superresolution of underwater imagery for improved visual perception,” arXiv preprint arXiv:2002.01155, 2020.[17] L. Zhang, B. He, Y. Song, and T. Yan, “Underwater image feature extraction and matching based on visual saliency detection,” in OCEANS 2016Shanghai. IEEE, 2016.[18] A. MaldonadoRamírez and L. A. TorresMéndez, “Robotic visual tracking of relevant cues in underwater environments with poor visibility conditions,” Journal of Sensors, 2016.[19] Microsoft azure cognitive services computer vision. Accessed on Nov. 2020.[Online]. Available: https://azure.microsoft.com/enus/services/cognitiveservices/computervision/[20] R. Zhao, W. Ouyang, H. Li, and X. Wang, “Saliency detection by multicontext deep learning,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 1265–1274.[21] G. Li and Y. Yu, “Visual saliency based on multiscale deep features,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015.[22] X. Li, L. Zhao, L. Wei, M.H. Yang, F. Wu, Y. Zhuang, H. Ling, and J. Wang, “Deep saliency: Multitask deep neural network model for salient object detection,” IEEE transactions on image processing, vol. 25, no. 8, pp. 3919–3930, 2016.[23] L. Wang, L. Wang, H. Lu, P. Zhang, and X. Ruan, “Saliency detection with recurrent fully convolutional networks,” in European conference on computer vision. Springer, 2016, pp. 825–841.[24] D. A. Klein and S. Frintrop, “Centersurround divergence of feature statistics for salient object detection,” in 2011 International Conference on Computer Vision IEEE, 2011.[25] M.M. Cheng, N. J. Mitra, X. Huang, P. H. Torr, and S.M. Hu, “Global contrast based salient region detection,” IEEE transactions on pattern analysis and machine intelligence, 2014.[26] Z. Wang, D. Xiang, S. Hou, and F. Wu, “Backgrounddriven salient object detection,” IEEE transactions on multimedia, 2016.[27] Ç. Aytekin, H. Possegger, T. Mauthner, S. Kiranyaz, H. Bischof, and M. Gabbouj, “Spatiotemporal saliency estimation by spectral foreground detection,” IEEE Transactions on Multimedia, 2017.[28] H. Peng, B. Li, W. Xiong, W. Hu, and R. Ji, “Rgbd salient object detection: a benchmark and algorithms,” in European conference on computer vision. Springer, 2014, pp. 92–109.[29] J. Han, H. Chen, N. Liu, C. Yan, and X. Li, “Cnnsbased rgbd saliency detection via crossview transfer and multiview fusion,” IEEE transactions on cybernetics, vol. 48, no. 11, pp. 3171–3183, 2017.[30] Y.T. Peng, X. Zhao, and P. C. Cosman, “Single underwater image enhancement using depth estimation based on blurriness,” in 2015 IEEE International Conference on Image Processing (ICIP). IEEE, 2015, pp. 4952–4956.[31] D. V. Ruiz, B. A. Krinski, and E. Todt, “Anda: A novel data augmentation technique applied to salient object detection,” in 2019 19th International Conference on Advanced Robotics (ICAR). IEEE, 2019.[32] Q. Hou, M.M. Cheng, X. Hu, A. Borji, Z. Tu, and P. H. Torr, “Deeply supervised salient object detection with short connections,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 3203–3212.[33] G. Lee, Y.W. Tai, and J. Kim, “Deep saliency with encoded low level distance map and high level features,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 660–668.[34] N. Liu and J. 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國立政治大學
資訊科學系
108753209資料來源 http://thesis.lib.nccu.edu.tw/record/#G0108753209 資料類型 thesis dc.contributor.advisor 彭彥璁 zh_TW dc.contributor.advisor Peng, Yan-Tsung en_US dc.contributor.author (Authors) 林祐丞 zh_TW dc.contributor.author (Authors) Lin, Yu-Cheng en_US dc.creator (作者) 林祐丞 zh_TW dc.creator (作者) Lin, Yu-Cheng en_US dc.date (日期) 2021 en_US dc.date.accessioned 1-Nov-2021 12:00:43 (UTC+8) - dc.date.available 1-Nov-2021 12:00:43 (UTC+8) - dc.date.issued (上傳時間) 1-Nov-2021 12:00:43 (UTC+8) - dc.identifier (Other Identifiers) G0108753209 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/137677 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊科學系 zh_TW dc.description (描述) 108753209 zh_TW dc.description.abstract (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。 zh_TW dc.description.abstract (摘要) 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. en_US dc.description.tableofcontents 摘要 iAbstract iiContents iiiList of Figures vList of Tables vi1 INTRODUCTION 12 RELATED WORKS 62.1 Deep SOD models 62.2 Data augmentation for SOD 83 PROPOSED DATASET 103.1 Dataset for underwater SOD 103.2 Dataset analysis and comparison 104 PROPOSED METHOD 144.1 Blurrinessguided SOD 144.1.1 Blurriness map generation 144.1.2 Blurrinessguided SOD model 164.2 Blurrinessguided augmentation for underwater SOD 175 EXPERIMENTS 205.1 Implementation and Experimental Setup 205.2 Evaluation Metrics 205.2.1 PrecisionRecall (PR) curve 205.2.2 Fmeasure family 215.2.3 Mean absolute error (MAE) 215.2.4 Smeasure 215.2.5 Emeasure 225.3 Experimental results 225.3.1 Blurrinessguided underwater SOD 225.3.2 FocusAugment 225.4 Ablation sutdy 255.4.1 Blurrinessguided underwater SOD 255.4.2 FocusAugment 256 Conclusion 29Reference 30 zh_TW dc.format.extent 4341676 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0108753209 en_US dc.subject (關鍵詞) 顯著物偵測 zh_TW dc.subject (關鍵詞) 資料擴增 zh_TW dc.subject (關鍵詞) 深度學習 zh_TW dc.subject (關鍵詞) Underwater salient object detection en_US dc.subject (關鍵詞) Data augmentation en_US dc.subject (關鍵詞) Deep learning en_US dc.title (題名) 水下顯著物目標檢測 zh_TW dc.title (題名) Underwater Salient Object Detection en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) [1] D. V. Ruiz, B. A. Krinski, and E. Todt, “Ida: Improved data augmentation applied to salient object detection,” in 2020 33rd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI). IEEE, 2020.[2] Y. Pang, X. Zhao, L. Zhang, and H. Lu, “Multiscale interactive network for salient object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020.[3] D. Zhang, D. Meng, L. Zhao, and J. Han, “Bridging saliency detection to weakly supervised object detection based on selfpaced curriculum learning,” arXiv preprint arXiv:1703.01290, 2017.[4] Z. Ren, S. Gao, L.T. Chia, and I. W.H. Tsang, “Regionbased saliency detection and its application in object recognition,” IEEE Transactions on Circuits and Systems for Video Technology, 2013.[5] S. P. Bharati, S. Nandi, Y. Wu, Y. Sui, and G. Wang, “Fast and robust object tracking with adaptive detection,” in 2016 IEEE 28th international conference on tools with artificial intelligence (ICTAI). IEEE, 2016.[6] H. Lee and D. Kim, “Salient regionbased online object tracking,” in 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2018. 30[7] M. Cornia, L. Baraldi, G. Serra, and R. Cucchiara, “Paying more attention to saliency: Image captioning with saliency and context attention,” ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 2018.[8] H. Hadizadeh and I. V. Bajić, “Saliencyaware video compression,” IEEE Transactions on Image Processing, vol. 23, no. 1, pp. 19–33, 2013.[9] Q.G. Ji, Z.D. Fang, Z.H. Xie, and Z.M. Lu, “Video abstraction based on the visual attention model and online clustering,” Signal Processing: Image Communication, vol. 28, no. 3, pp. 241–253, 2013.[10] Y. Ban and K. Lee, “Reenrichment learning: Metadata saliency for the evolutive personalization of a recommender system,” Applied Sciences, vol. 11, no. 4, p. 1733, 2021.[11] J. Zhang, X. Yu, A. Li, P. Song, B. Liu, and Y. Dai, “Weaklysupervised salient object detection via scribble annotations,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 12 546–12 555.[12] P. Siva, C. Russell, T. Xiang, and L. Agapito, “Looking beyond the image: Unsupervised learning for object saliency and detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2013, pp. 3238–3245.[13] L. Wang, H. Lu, Y. Wang, M. Feng, D. Wang, B. Yin, and X. Ruan, “Learning to detect salient objects with imagelevel supervision,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 136–145.[14] D.P. Fan, T. Li, Z. Lin, G.P. Ji, D. Zhang, M.M. Cheng, H. Fu, and J. Shen, “Rethinking cosalient object detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021.[15] M. J. Islam, R. Wang, K. de Langis, and J. Sattar, “Svam: Saliencyguided visual attention modeling by autonomous underwater robots,” arXiv preprint arXiv:2011.06252, 2020.[16] M. J. Islam, P. Luo, and J. Sattar, “Simultaneous enhancement and superresolution of underwater imagery for improved visual perception,” arXiv preprint arXiv:2002.01155, 2020.[17] L. Zhang, B. He, Y. Song, and T. Yan, “Underwater image feature extraction and matching based on visual saliency detection,” in OCEANS 2016Shanghai. IEEE, 2016.[18] A. MaldonadoRamírez and L. A. TorresMéndez, “Robotic visual tracking of relevant cues in underwater environments with poor visibility conditions,” Journal of Sensors, 2016.[19] Microsoft azure cognitive services computer vision. Accessed on Nov. 2020.[Online]. Available: https://azure.microsoft.com/enus/services/cognitiveservices/computervision/[20] R. Zhao, W. Ouyang, H. Li, and X. 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