Cited By
View all- Zhou HKim SKim SKim CKang SKim H(2023)Instance Segmentation of Shrimp Based on Contrastive LearningApplied Sciences10.3390/app1312697913:12(6979)Online publication date: 9-Jun-2023
Automatic Detection of Peeled Shrimp Based on Image Enhancement and Convolutional Neural Networks
Pages 439 - 450
Abstract
The shrimp peeling is an important step in the shrimp processing, and the detection of the peeled shrimp is the key criterion for measuring the effect of automatic peeling of shrimp. The detection of the peeled shrimp using on conveyor belt shrimp images is a complex task in the actual production. Shrimp images pose a big challenge duo to the similarity between peeled and shell-on shrimp. In this study, a hybrid recognition method combined image enhancement strategy and YOLO V4 deep learning method is proposed for the peeled shrimp detection in peeling processing. The image enhancement strategy used several image enhancement methods for the original shrimp images preprocessing to simulation the reality shrimp peeling environment. A comparative analysis between preprocessing images and original image used for the training the peeled shrimp detection models based on SSD and YOLO methods is performed. The results showed that preprocessed images can improve the accuracy and the robustness of the detection model obviously. This study also used the enhancement image as the training data set to train 11 detection models for the peeled or shell-on shrimp detection. The results showed that YOLO V4 has the highest accuracy with the enhancement image, can reach about 99.10% for the shrimp peeling detection. Therefore, the proposed method is useful for shrimp peeling detection task and quality measurement of production lines.
References
[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).
[2]
Yu X, Yu X, Wen S, Yang J and Wang J, Using deep learning and hyperspectral imaging to predict total viable count (TVC) in peeled Pacific white shrimp,J FOOD MEAS CHARACT,13,2082-2094 (2019).
[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).
[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).
[5]
Pan PM, Li JP, Lv GL, Yang H, Zhu SM and Lou JZ, Prediction of shelled shrimp weight by machine vision,J Zhejiang Univ Sci B,10, 589-594 (2009).
[6]
Tan J, Complex object detection using deep proposal mechanism,ENG APPL ARTIF INTEL,87, 103234 (2020).
[7]
Bilal M and Hanif MS, Benchmark Revision for HOG-SVM Pedestrian Detector Through Reinvigorated Training and Evaluation Methodologies,IEEE T INTELL TRANSP,21, 1277-1287 (2020).
[8]
Mendieta M, Panchana F and Andrade B, Organ identification on shrimp histological images: A comparative study considering CNN and feature engineering, 2018 IEEE Third Ecuador Technical Chapters Meeting (ETCM), IEEE. 1-6, (2018).
[9]
Liu Z, Jia X and Xu X, Study of shrimp recognition methods using smart networks,COMPUT ELECTRON AGR,165, 104926 (2019).
[10]
Kaewchote J, Janyong S and Limprasert W, Image recognition method using Local Binary Pattern and the Random forest classifier to count post larvae shrimp,Agriculture and Natural Resources,52, 371-376 (2018).
[11]
Nguyen K, Nguyen C, Wang C and Wang J, Two-Phase Instance Segmentation for Whiteleg Shrimp Larvae Counting, IEEE International Conference on Consumer Electronics (ICCE), 364-366 (2020).
[12]
Alçiçek Z and Balaban MÖ, Development and application of “The Two Image” method for accurate object recognition and color analysis,J FOOD ENG,111, 46-51 (2012).
[13]
Liu Z, Soft-shell Shrimp Recognition Based on an Improved AlexNet for Quality Evaluations,J FOOD ENG,266,109698 (2020).
[14]
Yu X, Tang L, Wu X and Lu H, Nondestructive Freshness Discriminating of Shrimp Using Visible/Near-Infrared Hyperspectral Imaging Technique and Deep Learning Algorithm,FOOD ANAL METHOD,11, 768-780 (2018).
[15]
Redmon J, Divvala S, Girshick R and Farhadi A, You Only Look Once: Unified, Real-Time Object Detection, IEEE Conference on Computer Vision and Pattern Recognition,(2016).
[16]
Kuznetsova A, Maleva T and Soloviev V, Using YOLOv3 Algorithm with Pre- and Post-Processing for Apple Detection in Fruit-Harvesting Robot,Agronomy,10,1016 (2020).
[17]
Jiao L, Dong S, Zhang S, Xie C and Wang H, AF-RCNN: An anchor-free convolutional neural network for multi-categories agricultural pest detection,COMPUT ELECTRON AGR,174(2020)105522.
[18]
Liu C, Hu SC, Wang C, Lafata K and Yin FF, Automatic detection of pulmonary nodules on CT images with YOLOv3: development and evaluation using simulated and patient data,Quant Imaging Med Surg,10, 1917-1929 (2020).
[19]
Choudhary M, Tiwari V and Uduthalapally V, Iris presentation attack detection based on best-k feature selection from YOLO inspired RoI, Neural Computing and Applications, 1-21 (2020).
[20]
Ge L, Dan D and Li H, An accurate and robust monitoring method of full‐bridge traffic load distribution based on YOLO‐v3 machine vision,Structural Control and Health Monitoring,27, (2020).
[21]
Umme Fawzia Rahim and Hiroshi Mineno, Tomato Flower Detection and Counting in Greenhouses Using Faster Region-Based Convolutional Neural Network, Journal of Image and Graphics, 8(4), 107-113, (2020).
[22]
Ryo Hasegawa, Yutaro Iwamoto, and Yen-Wei Chen, Robust Japanese Road Sign Detection and Recognition in Complex Scenes Using Convolutional Neural Networks, Journal of Image and Graphics, 8(3), 59-66, (2020).
[23]
Bochkovskiy A, Wang C and Liao HM,YOLOv4: Optimal Speed and Accuracy of Object Detection,(2020).
[24]
Xue Y, Ye J, Zhou Q, Antani S and Xue Z, Selective Synthetic Augmentation with HistoGAN for Improved Histopathology Image Classification,MED IMAGE ANAL, 67, 101816 (2020).
[25]
Li M, Zhang Z, Lei L, Wang X and Guo X, Agricultural Greenhouses Detection in High-Resolution Satellite Images Based on Convolutional Neural Networks: Comparison of Faster R-CNN, YOLO v3 and SSD,SENSORS-BASEL,20,4938 (2020).
[26]
Kumar A, Zhang ZJ and Lyu H, Object detection in real time based on improved single shot multi-box detector algorithm, EURASIP J WIREL COMM, 1, (2020).
[27]
Cheng Z and Zhang F, Flower End-to-End Detection Based on YOLOv4 Using a Mobile Device,Wireless Communications and Mobile Computing,1-9 (2020).
[28]
Yadav SS and Jadhav SM, Detection of common risk factors for diagnosis of cardiac arrhythmia using machine learning algorithm, EXPERT SYST APPL,163, 113807 (2021).
- Automatic Detection of Peeled Shrimp Based on Image Enhancement and Convolutional Neural Networks
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Published In
March 2022
809 pages
ISBN:9781450396110
DOI:10.1145/3532213
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Association for Computing Machinery
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Publication History
Published: 13 July 2022
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- Research-article
- Research
- Refereed limited
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- Next generation precision aquaculture: R&D on intelligent measurement, control and equipment technologies
- the Fundamental Research Funds for the Central Universities
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ICCAI '22
ICCAI '22: 2022 8th International Conference on Computing and Artificial Intelligence
March 18 - 21, 2022
Tianjin, China
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View all- Zhou HKim SKim SKim CKang SKim H(2023)Instance Segmentation of Shrimp Based on Contrastive LearningApplied Sciences10.3390/app1312697913:12(6979)Online publication date: 9-Jun-2023
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