Automatic Detection of Peeled Shrimp Based on Image Enhancement and Convolutional Neural Networks

Author:

Yu Huihui1,Liu Xiong2,Qin Hanxiang3,Yang Ling3,Chen Yingyi3

Affiliation:

1. School of Information Science&Technology, Beijing Forestry University, China

2. College of Mechatronical&Electrical Engineering, Hebei Agricultural University, China

3. College of Information and Electrical Engineering, China Agricultural University, China and National Innovation Center for Digital Fishery, China Agricultural University, China

Funder

Next generation precision aquaculture: R&D on intelligent measurement, control and equipment technologies

the Fundamental Research Funds for the Central Universities

Publisher

ACM

Reference28 articles.

1. Hu W , Wu H , Zhang Y , Zhang S and Lo C , Shrimp recognition using ShrimpNet based on convolutional neural network , J AMB INTEL HUM COMP , 12 , 1 - 8 ( 2020 ). Hu W, Wu H, Zhang Y, Zhang S and Lo C, Shrimp recognition using ShrimpNet based on convolutional neural network, J AMB INTEL HUM COMP,12,1-8 (2020).

2. Using deep learning and hyperspectral imaging to predict total viable count (TVC) in peeled Pacific white shrimp

3. Hao S , Yang X and Huang H , , Shrimp shelling processing status and convenient pretreatment shelling technology , South China Fisheries Science , 16 , 121 - 128 ( 2020 ). Hao S, Yang X and Huang H, , Shrimp shelling processing status and convenient pretreatment shelling technology,South China Fisheries Science,16, 121-128 (2020).

4. Daokuan M , Li G , Ling G , Xihe Z and Linquan Z , The force analysis of the roller type shrimp peeling device during the peeling process , Modern Agricultural Equipment , 41 , 64 - 68 ( 2020 ). Daokuan M, Li G, Ling G, Xihe Z and Linquan Z, The force analysis of the roller type shrimp peeling device during the peeling process,Modern Agricultural Equipment,41, 64-68 (2020).

5. Prediction of shelled shrimp weight by machine vision

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