參考文獻 |
[1]Kwang-Tsao Shao, "The Fish Database of Taiwan," TELDAP, 2014.
[2]P. Patel, A. Thakkar, "The upsurge of deep learning for computer vision applications," Indonesian Journal of Electrical Engineering and Computer Science, vol. 10, no.1, pp. 538-548, 2020.
[3]A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet classification with deep convolutional neural networks," in Proceedings of the 25th Neural Information Processing Systems, vol. 1, pp. 1097–1105, 2012.
[4]K. He, X. Zhang, S. Ren, J. Sun, "Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification," arXiv preprint arXiv:.01852, 2015.
[5]I. Sharmin, N. F. Islam, I. Jahan, T. A. Joye, and M. T. Habib, "Machine vision based local fish recognition," SN Applied Sciences, vol. 1, pp. 1529, 2019.
[6]U. Andayani, A. Wijaya, R. Rahmat, B. Siregar, M. Syahputra, "Fish species classification using probabilistic neural network," Journal of Physics: Conference Series, vol. 1235, no. 1, pp. 012094, 2019.
[7]S. N. M. Rum, F. A. Z. Nawawi, "FishDeTec: A Fish Identification Application using Image Recognition Approach," International Journal of Advanced Computer Science and Applications, vol. 12, no. 3, pp. 102-106, 2021.
[8]A. Dhillon, G. K. Verma, "Convolutional neural network: A review of models, methodologies and applications to object detection," Progress in Artificial Intelligence, vol. 9, no. 2, pp. 85-112, 2020.
[9]M. Yusup, M. Iqbal and I. Jaya, "Real-time reef fishes identification using deep learning," IOP Conference Series: Earth and Environmental Science, vol. 429, pp. 012046, 2020.
[10]Redmon and A. Farhadi, "Yolov3: An incremental improvement," arXiv preprint arXiv:.02767, 2018.
[11]M. Knausgard, A. Wiklund, T. K. Sørdalen, K. T. Halvorsen, A. R. Kleiven, L. Jiao, M. Goodwin, "Temperate fish detection and classification a deep learning based approach," arXiv:07518, 2020.
[12]P. R. Hegde, M. M. Shenoy, and B. H. Shekar, "Comparison of Machine Learning Algorithms for Skin Disease Classification Using Color and Texture Features," International Conference on Advances in Computing, Communications and Informatics, pp. 1825–1828, 2018.
[13]L. Jiao, F. Zhang, F. Liu, S. Yang, L. Li, Z. Feng, R. Qu, "A Survey of Deep Learning-based Object Detection," IEEE Access, vol. 7, pp. 128837-128868, 2019.
[14]W. Liu, D. Anguelov, D. Erhan, C. Szegedy, and S. Reed, "SSD: Single shot multibox detector," arXiv preprint arXiv:02325, 2015.
[15]J. Redmon and A. Farhadi, "YOLO9000: better, faster, stronger," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 7263-7271.
[16]J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You only look once: Unified, real-time object detection," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 779-788.
[17]A. Bochkovskiy, C. Y. Wang, and H. Y. M.Liao, "YOLOv4: Optimal Speed and Accuracy of Object Detection," arXiv preprint arXiv:.10934, 2020.
[18]R. Girshick, J. Donahue, T. Darrell, and J. Malik, "Rich feature hierarchies for accurate object detection and semantic segmentation," in Proceedings of the 72 IEEE conference on computer vision and pattern recognition, 2014, pp. 580- 587.
[19]R. Girshick, "Fast r-cnn," in Proceedings of IEEE international conference on computer vision, 2015, pp. 1440-1448.
[20]S. Ren, K. He, R. Girshick, and J. Sun, "Faster r-cnn: Towards real-time object detection with region proposal networks," in Advances in neural information processing systems, 2015, pp. 91-99.
[21]K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770-778.
[22]T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie, "Feature pyramid networks for object detection," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 2117-2125.
[23]T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollar, "Focal loss for dense object detection," arXiv preprint arXiv:02002, 2017.
[24]F.Y. Osisanwo, J. E. T. Akinsola, O. Awodele, J. O. Hinmikaiye, O. Olakanmi, and J. Akinjobi, "Supervised Machine Learning Algorithms: Classification and Comparison," International Journal of Computer Trends and Technology, vol. 48, no. 3, pp. 128-138, 2017.
[25]A.F. Agarap, "An Architecture Combining Convolutional Neural Network (CNN) and Support Vector Machine (SVM) for Image Classification," arXiv preprint arXiv:03541, 2017.
[26]M. AI-Qatf, Y. Lasheng, M. AI-Habib and K. Al-Sabahi, "Deep learning approach combining sparse autoencoder with SVM for network intrusion detection," IEEE Access, no. 6, pp. 52843-52856, 2018.
[27]Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition," Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, 1998.
[28]D. E. Rumelhart, G. E. Hinton, and R. J. Williams, "Learning internal representations by error propagation," California Univ San Diego La Jolla Inst for Cognitive Science, 1985.
[29]A. F. Agarap, "Deep Learning using Rectified Linear Units(ReLU)," arXiv preprint arXiv:08375, 2018.
[30]N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhut-dinov, "Dropout: A simple way to prevent neural networks from overfitting," Journal of Machine Learning Research, vol. 15, pp. 1929-1958, 2014.
[31]L. Perez and J. Wang, "The effectiveness of data augmentation in image classification using deep learning," arXiv preprint arXiv:04621, 2017.
[32]K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," arXiv preprint arXiv:91556, 2014.
[33]C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, "Going deeper with convolutions," arXiv preprint arXiv:4842, 2014.
[34]J. Bromley, I. Guyon, Y. LeCun, E. Sckinger and R. Shah, "Signature Verification using a "Siamese" Time Delay Neural Network," Proceedings of the 7th Annual Neural Information Processing Systems, vol. 7, no. 4, pp. 669-687, 1994.
[35]C.-H. Chen, M.-Y. Lin, and X.-C. Guo, "High-level modeling and synthesis of smart sensor networks for Industrial Internet of Things," Computers & Electrical Engineering, vol. 61, pp. 48-66, 2017.
[36]Ching-Han Chen, Lu-Hsuan Chen, and Chin-Yi Chen, "Automatic Fish Segmentation and Recognition in Taiwan Fish Market Using Deep Learning Techniques," Journal of Imaging Science and Technology, vol. 65, no. 4, pp. 040403-1–040403-10, 2021. |