第五章 結論與未來研究方向
5.2 未來研究方向
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次利用過往收集的資料集，大幅下降收集訓練資料及模型訓練的時間，即可滿足 在不同場景偵測物體的需求，達到相對簡單的物件偵測訓練。
5.2. 未來研究方向
在水下攝影的時候，攝影者與潛伴的距離難控制且水下活動不易，使得拍攝 到的潛水員影像狀況不一，除了會有三個方向（上下、左右、前後）而導致拍攝 者在不同距離及不同視角拍攝到潛水員，加上潛水員裝備彼此有差異，拍攝到局
部或全部皆會影響訓練結果，導致AP 較低，因此未來可針對此狀況進行討論而
提高偵測潛水員之準確度。
在本論文的第二章提到，光散射有對於增加物體的照明有一定幫助，但事實 上也會降低物體和背景之間的對比度而使的得到的圖像變的模糊，這個問題與光
源照度強弱無關，並不會因為較強的光源就有所改善，而此問題也會造成 mAP
下降，因此可由信噪比等方面下手，針對模糊的狀況做出相對應的處理，使得影 像品質提高。
此外，本論文為基於pix2pix model，而此模型在影像解析度上有很大的限制，
一般是輸出256x256 的影像（本研究為 512x512），小解析度雖然可讓速度較快，
但也會影響偵測率，而如果強制輸出高解析度(如 2048x1024)，會使得輸出影像 品質下降，且在訓練穩定度也不夠，因此未來可針對高解析度進行研究，例如探 討pix2pixHD[20] model 作法可行性。
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[3] Klemm et al., “Exploring Our Fluid Earth is based on the nationally recognized Fluid Earth/Living Ocean (FELO) aquatic science curriculum.” 1995.
[4] Girshick, Ross, et al. "Rich feature hierarchies for accurate object detection and semantic segmentation." Proceedings of the IEEE conference on computer vision and pattern recognition. 2014.
[5] Ren, Shaoqing, et al. "Faster r-cnn: Towards real-time object detection with region proposal networks." Advances in neural information processing systems.
2015.
[6] Redmon, Joseph, et al. "You only look once: Unified, real-time object
detection." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
[7] Steiner, A. "Understanding the Basics of Underwater Lighting." Ocean News &
Technology 19.4 (2013): 10-12.
[8] Liu, Wei, et al. "Ssd: Single shot multibox detector." European conference on computer vision. Springer, Cham, 2016.
[9] Radford, Alec, Luke Metz, and Soumith Chintala. "Unsupervised representation learning with deep convolutional generative adversarial networks." arXiv preprint arXiv:1511.06434 (2015).
[10] Kuznetsova, Alina, et al. "The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale."
(2020).
‧
[11] Zhu, Jun-Yan, et al. "Unpaired image-to-image translation using cycle-consistent adversarial networks." Proceedings of the IEEE international conference on computer vision. 2017.
[12] Sato, Kazunori. "An inside look at google bigquery." White paper, URL:
https://cloud. google. com/files/BigQueryTechnicalWP. pdf (2012).
[13] Mirza, Mehdi, and Simon Osindero. "Conditional generative adversarial nets." arXiv preprint arXiv:1411.1784 (2014).
[14] Isola, Phillip, et al. "Image-to-image translation with conditional adversarial networks." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.
[15] Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. "U-net: Convolutional networks for biomedical image segmentation." International Conference on Medical image computing and computer-assisted intervention. Springer, Cham, 2015.
[16] Panaretos, Victor M., and Yoav Zemel. "Statistical aspects of Wasserstein distances." Annual review of statistics and its application 6 (2019): 405-431.
[17] Arjovsky, Martin, Soumith Chintala, and Léon Bottou. "Wasserstein gan." arXiv preprint arXiv:1701.07875 (2017).
[18] Gulrajani, Ishaan, et al. "Improved training of wasserstein gans." Advances in neural information processing systems. 2017.
[19] Wu, N., and V. Rathod. "Tensorflow detection model zoo." (2017).
[20] Wang, Ting-Chun, et al. "High-resolution image synthesis and semantic manipulation with conditional gans." Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.
‧
[21] Pedersen, Malte, et al. "Detection of marine animals in a new underwater dataset with varying visibility." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 2019.
[22] Li, Xirong, et al. "COCO-CN for Cross-Lingual Image Tagging, Captioning, and Retrieval." IEEE Transactions on Multimedia 21.9 (2019): 2347-2360.
[23] Kornblith, Simon, Jonathon Shlens, and Quoc V. Le. "Do better imagenet
models transfer better?." Proceedings of the IEEE conference on computer vision and pattern recognition. 2019.
[24] Teama, Tarek, et al. "Real Time Object Detection Based on Deep Neural Network." International Conference on Intelligent Robotics and Applications.
Springer, Cham, 2019.T Lin, Aire, et al. "Focal loss for dense object detection."
IEEE Trans. Pattern Anal. Mach. Intell. 42.2 (2020): 318-327.
[25] Lin, Aire, et al. "Focal loss for dense object detection." IEEE Trans. Pattern Anal. Mach. Intell. 42.2 (2020): 318-327.
[26] Akkaynak, Derya, and Tali Treibitz. "Sea-thru: A method for removing water from underwater images." Proceedings of the IEEE conference on computer vision and pattern recognition. 2019.
[27] Kolsur, Anoop, Sandeep Awale, and Nagraj Ullagaddi. "POV: Persistence of Vision."
[28] Mittal, Anish, Rajiv Soundararajan, and Alan C. Bovik. "Making a “completely blind” image quality analyzer." IEEE Signal processing letters 20.3 (2012): 209-212.
[29] Bochkovskiy, Alexey, Chien-Yao Wang, and Hong-Yuan Mark Liao. "YOLOv4:
Optimal Speed and Accuracy of Object Detection." arXiv preprint arXiv:2004.10934 (2020).