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张士薇,等 水产学报, 2025, 49(5): 059117
2023, 25(4): 214-226 (in Chinese). He Z P, Gong G R, Xiong Y, et al. A phenotypic measurement
[34] 孔杰, 栾生, 谭建, 等. 对虾选择育种研究进展 [J]. 中国海洋 and weight prediction model of Pelteobagrus fulvidraco based
大学学报, 2020, 50(9): 81-97. on computer vision[J]. Acta Hydrobiologica Sinica, 2024,
Kong J, Luan S, Tan J, et al. Progress of study on penaeid 48(7): 1149-1158 (in Chinese).
shrimp selective breeding[J]. Periodical of Ocean University of [41] Redmon J, Divvala S, Girshick R, et al. You Only Look Once:
China, 2020, 50(9): 81-97 (in Chinese). unified, real-time object detection[C]//IEEE. Proceedings of
[35] Sun K, Xiao B, Liu D, et al. Deep high-resolution representa- 2016 IEEE Conference on Computer Vision and Pattern Recog-
tion learning for human pose estimation[C]//IEEE. Proceedings nition. Las Vegas: IEEE, 2016: 779-788.
of 2019 IEEE/CVF Conference on Computer Vision and Pat- [42] 宋自根, 张佳彬, 覃学标, 等. 一种基于 Mask-RCNN 图像分
tern Recognition. Long Beach: IEEE, 2019: 5693-5703. 割的头足类动物角质颚色素沉积量化方法 [J]. 渔业现代化,
[36] 杨爱萍, 田鑫, 杨炳旺, 等. 基于多特征融合的单幅水下图像 2021, 48(5): 70-78.
清晰化 [J]. 天津大学学报 (自然科学与工程技术版), 2018, Song Z G, Zhang J B, Qin X B, et al. A Mask-RCNN based
51(10): 1031-1041. quantification method for pigmentation of cephalopod beaks[J].
Yang A P, Tian X, Yang B W, et al. Single underwater image Fishery Modernization, 2021, 48(5): 70-78 (in Chinese).
sharpening based on multi-feature fusion[J]. Journal of Tianjin [43] He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for
University (Science and Technology Edition), 2018, 51(10): image recognition[C]//IEEE. Proceedings of 2016 IEEE Con-
1031-1041 (in Chinese). ference on Computer Vision and Pattern Recognition. Las
[37] 徐岩, 孙美双. 基于多特征融合的卷积神经网络图像去雾算 Vegas: IEEE, 2016: 770-778.
法 [J]. 激光与光电子学进展, 2018, 55(3): 031012. [44] Lin T Y, Dollár P, Girshick R, et al. Feature pyramid networks
Xu Y, Sun M S. Convolution neural network image defogging for object detection[C]//IEEE. Proceedings of 2017 IEEE Con-
based on multi-feature fusion[J]. Laser & Optoelectronics Pro- ference on Computer Vision and Pattern Recognition. Hon-
gress, 2018, 55(3): 031012 (in Chinese). olulu: IEEE, 2017: 2117-2125.
[38] Zhou H, Kim S H, Kim S C, et al. Size estimation for shrimp [45] He K M, Gkioxari G, Dollár P, et al. Mask R-CNN[C]//IEEE.
using deep learning method[J]. Smart Media Journal, 2023, Proceedings of 2017 IEEE International Conference on Com-
12(3): 112-119. puter Vision. Venice, Italy: IEEE, 2017: 2961-2969.
[39] 鲍镇宁, 于洋, 李富花. 基于 Faster R-CNN 的对虾生长性状 [46] Prajapati S D, Ujjania N C. Study on length weight relationship
表型高通量测定技术的建立及应用 [J]. 水生生物学报, 2023, and condition factor of whiteleg shrimp Litopenaeus vannamei
47(10): 1576-1584. (Boone, 1931) cultured in earthen pond, Khambhat (Gujarat)[J].
Bao Z N, Yu Y, Li F H. The establishment and application of a International Journal of Fauna and Biological Studies, 2021,
fast phenotypic determination technique based on Faster R- 8(1): 67-70.
CNN for growth traits in shrimp[J]. Acta Hydrobiologica Sin- [47] 李玉虎, 张志怀, 宋芹芹, 等. 凡纳滨对虾新品系体形性状对
ica, 2023, 47(10): 1576-1584 (in Chinese). 其体质量的影响 [J]. 热带生物学报, 2014, 5(4): 307-311.
[40] 何志鹏, 巩高瑞, 熊阳, 等. 基于计算机视觉的黄颡鱼表型特 Li Y H, Zhang Z H, Song Q Q, et al. Effect of growth traits on
征测量和体重预测模型研究 [J]. 水生生物学报, 2024, 48(7): body weight of the new breeds of Litopenaeus vannamei[J].
1149-1158. Journal of Tropical Biology, 2014, 5(4): 307-311 (in Chinese).
https://www.china-fishery.cn 中国水产学会主办 sponsored by China Society of Fisheries
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