A multi-scale features-based method to detect Oplegnathus

Information Processing in Agriculture - Tập 8 - Trang 437-445 - 2021
Jun Yue1, Huihui Yang1, Shixiang Jia1, Qing Wang2, Zhenbo Li3, Guangjie Kou1, Ruijia Ba1
1School of Information and Electrical Engineering, Ludong University, Yantai 264025, China
2School of Civil Engineering, Ludong University, Yantai 264025, China
3School of Information and Electrical Engineering, China Agricultrual University, Beijing 100083, China

Tài liệu tham khảo

Artificial breeding technology of Oplegnathus punctatus in Laizhou, Yantai city, Shandong province fills the gap in China. China's aquatic product. 2014; (08): 35 Sun, 2016, Models for estimating feed intake in aquaculture: A review. Computers Electronics, Agriculture., 425 Hao M, Yu H, Li D. The measurement of fish size by machine vision-a review. In: International Conference on Computer and Computing Technologies in Agriculture. 201. p. 15-32. Abd Ulmoula M.Z. Biomass estimation of fish using deep networks and stereo vision. 2020. Su, 2020, Visual analysis of fish feeding intensity for smart feeding in aquaculture using deep learning, International Workshop on Advanced Imaging Technology (IWAIT), 10.1117/12.2566902 Shi, 2020, An automatic method of fish length estimation using underwater stereo system based on LabVIEW. Computers Electronics, Agriculture. Li, 2019, Nonintrusive methods for biomass estimation in aquaculture with emphasis on fish: a review, Reviews in Aquaculture Li, 2020, Automatic recognition methods of fish feeding behavior in aquaculture: A review, Aquaculture Lowe, 2004, Distinctive image features from scale-invariant keypoints, Int J Comput Vision, 91, 10.1023/B:VISI.0000029664.99615.94 Dalal N, Triggs B. Histograms of oriented gradients for human detection. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05). 2005. p. 886-893. Lecun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition. 1998. p. 2278-2324. Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks.In: Advances in neural information processing systems. 2012. p.1097-1105. Taheri-Garavand, 2020, Smart deep learning-based approach for non-destructive freshness diagnosis of common carp fish, J Food Eng, 10.1016/j.jfoodeng.2020.109930 Yu, 2020, Segmentation and measurement scheme for fish morphological features based on Mask R-CNN. Information Processing, Agriculture. Redmon, 2016, You only look once: Unified, real-time object detection.In, 779 Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation.In Proceedings of the IEEE conference on computer vision and pattern recognition. 2014. p. 580-587. Ren S, He K, Girshick R, et al. Faster r-cnn: Towards real-time object detection with region proposal networks.In: Advances in neural information processing systems. 2015. p. 91-99. Sung, 2017, Vision based real-time fish detection using convolutional neural network, OCEANS, 1 Yuan C,Zhang S, An underwater fish target detection method based on Faster r-cnn and image enhancement.Journal of dalian ocean university. 10.16535/j.cnki.dlhyxb.2019-146. Link: https://kns.cnki.net/KCMS/detail/21.1575.s.20191202.1131.003.html Li X, Tang Y, Gao T. Deep but lightweight neural networks for fish detection.In: OCEANS 2017-Aberdeen. 2017. p.1-5 Liu, 2016, Ssd: Single shot multibox detector.In, 21 Ioffe S, Szegedy C J a P A. Batch normalization: Accelerating deep network training by reducing internal covariate shift, 2015 Reithaug, 2018, Employing Deep Learning for Fish Recognition, The University of Bergen Simonyan K, Zisserman A J a P A. Very deep convolutional networks for large-scale image recognition, 2014. Howard A G, Zhu M, Chen B, et al. Mobilenets: Efficient convolutional neural networks for mobile vision applications, 2017. Hung, 2019, SSD-Mobilenet Implementation for Classifying Fish Species, 399 Chen, 2017, Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs, 40, 834 Sandler, 2018, Mobilenetv 2: Inverted residuals and linear bottlenecks, 4510 Hu J, Shen L, Sun G. Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2018. p.7132-7141. Lin, 2017, Focal loss for dense object detection, 2980