811
Views
0
CrossRef citations to date
0
Altmetric
Original Article

Recognition and localization of freshwater fish heads and tails based on lightweight neural networks

, , , , , & show all
Pages 1290-1303 | Received 12 Dec 2022, Accepted 02 Apr 2023, Published online: 18 May 2023

References

  • Zhang, T.; Chen, E.; Xiao, W. A Fast Target Detection Method for Improving MobileNet_yolov3 Network. Small Mcom. Sys. 2021, 42(5), 7. DOI: 10.3969/j.issn.1000-1220.2021.05.018.
  • Hu, F. Establishment of a Comprehensive Evaluation System for Freshwater Fish Nutrition[d]. HZ. Agric. Univ. 2011. DOI: 10.7666/d.y2004238.
  • Wu, L.; Chen, L.; Wang, L.; Cai, D.; Zhang, Z.; Zhan, G.; Fang, Y. Analysis of Fatty Acid Composition and Its Nutritional Value of ten Freshwater Fish Species. Food Ind. 2017, 38(8), 269–271. DIO: CNKI: SUN: SPGY.0.2017-08-073.
  • Huang, W. Research on Fish Tail Removal Device Based on Machine Vision[d]. Wuhan Light Ind. Univ. 2021. DOI: 10.27776/d.cnki.gwhgy.2021.000028.
  • Liu, Y. Research on Fish Head and Fish Tail Positioning Technology Based on Machine Vision[d]. Wuhan Light Ind. Univ. 2021. DOI: 10.27776/d.cnki.gwhgy.2021.000313.
  • Li, M. Research on Head and Tail Cutting Device and Control Technology of Freshwater Fish Based on Machine Vision[d]. Wuhan Light Ind. Univ. 2021. DOI: 10.27776/d.cnki.gwhgy.2021.000117.
  • Wang, H. Research on Freshwater Fish Orientation and Head-Cutting Equipment[d]. Wuhan Light Ind. Univ. 2020. DOI: 10.27776/d.cnki.gwhgy.2020.000129.
  • Jia, B.; Shao, Z.; Wang, R.; Qu, Y.; Zhang, R.; Rao, K.; Jiang, A.; Liu, Y.; Guan, Y. Extraction and Analysis of Fish Behavioral Features Based on Machine Vision. J. Ecotoxicol. 2017, 12(05), 193–203. DOI: 10.7524/AJE.1673-5897.20161012001.
  • Li, K.; Wen, Y. Key Technology of Vision-Guided Automatic Head and Tail Removal System for Freshwater Fish. Food Mach. 2014, 30(05), 141–143. DOI: 10.13652/j.issn.1003-5788.2014.05.030.
  • Zhang, F.; Wan, P.; Zong, L.; Tan, H. Experiments and Analysis on the Mechanical Properties of the Cutting of the Head of Silver Carp. J. Huazhong Agric. Univ. 2016, 35(03), 122–127. DOI: 10.13300/j.cnki.hnlkxb.2016.03.020.
  • Chen, C.; Wu, Q.; Wu, Z.; Lv, T. An Automatic Fish Classification Method Based on Image Processing. Software Eng. 2018, 21(12), 7–11. DOI: 10.19644/j.cnki.issn2096-1472.2018.12.003.
  • Zhang, Z.; Niu, Z.; Zhao, S. Freshwater Fish Species Identification Based on Machine Vision Technology. J. Agric. Eng. 2011, 27(11), 388–392. DOI: 10.3969/j.issn.1002-6819.2011.11.072.
  • Pi, B.; Wang, Y. A Review of Traditional Machine Learning and Deep Learning for Expression Recognition. Software Guide. 2020, 19(06), 44–47. DOI: 10.11907/rjdk.192322.
  • Yao, R.; Gui, W.; Huang, Q. Freshwater Fish Species Identification Based on Machine Vision. Micro Mach. Appl. 2017, 36(24), 37–39. DOI: 10.19358/j.issn.1674-7720.2017.24.011.
  • Tanveer, A.; Salman, Q.; Syed, F.; Syed, A.; Razzaq, S.; Mubashir, S.; Muzammil, A.; Amir, U.; Israr, H.; Javeria, H., et al. Machine Learning Approach for Classification of Mangifera Indica Leaves Using Digital Image Analysis. Int. J. Food Prop. 2022, 25(1), 1987–1999. DOI: 10.1080/10942912.2022.2117822.
  • Fan, L.; Liu, Y.; Yu, X.; Lu, H. Computer Vision-Based Algorithm for Sport Fish Detection. J. Agric. Eng. 2011, 27(07), 226–230+394. DOI: 10.3969/j.issn.1002-6819.2011.07040.
  • Song, H.; Zhang, X.; Zheng, B. Vehicle Target Detection in Complex Scene Based on Deep Learning Methods. Computer Appl. Res. 2018, 35(4), 4. DOI: 10.3969/j.issn.1001-3695.2018.04.067.
  • Guo, G.; Lin, B.; Yang, X.; Zhang, X. Fish Detection Algorithm Based on Zebrafish Image Features. Appl. Opt. 2022, 43(02), 257–268. DOI: 10.5768/JAO202243.0202004.
  • Fang, S. Research on Deep Learning Based Method for Measuring Fish Phenotype Data[d]. Zhejiang Univ. 2021. DOI: 10.27461/d.cnki.gzjdx.2021.002100.
  • Wang, X. Y. Design and Implementation of Image-Based Marine Microalgae Identification System[d]; Dalian Ocean University: Dalian, 2020. DOI: 10.27821/d.cnki.gdlhy.2020.000001.
  • Liu, J.; Wang, X. W. Tomato Diseases and Pests Detection Based on Improved YOLOv3 Convolutional Neural Network. Front. Plant Sci. 2020, 11, 898–909. DOI: 10.3389/fpls.2020.00898.
  • Zhu, M.; Zhang, Z.; Huang, H.; Chen, Y.; Liu, Y.; Dong, T. Classification of Feeding Status of Bass Based on Lightweight Neural Network MobileNetv3-Small. J. Agric. Eng. 2021, 37(19), 165–172. DOI: 10.11975/j.issn.1002-6819.2021.19.019.
  • Yeh, C.; Chang, Y.; Alkhaleefah, M. YOLOv3-Based Matching Approach for Roof Region Detection from Drone Images. Remote Sens. 2021, 13(1), 127. DOI: 10.3390/rs13010127.
  • Ning, Z.; Mi, Z. Research on Surface Defect Detection Algorithm of Strip Steel Based on Improved YOLOV3. J. Phys. Conf. Ser. 2021, 1907(1), 012015. DOI: 10.1088/1742-6596/1907/1/012015.
  • Yang, Q.; Yu, L. Recognition of Taxi Violations Based on Semantic Segmentation of PSPNet and Improved YOLOv3. Hindawi Limited. 2021, 2021, 1–13. DOI: 10.1155/2021/4520190.
  • Singh, S.; Ahuja, U.; Kumar, M.; Kumar, K.; Sachdeva, M. Face Mask Detection Using YOLOv3 and Faster R-CNN Models: COVID-19 Environment. Multimedia Tools Appl. 2021, 2021(13), 1–16. DOI: 10.1007/s11042-021-10711-8.
  • Liu, X.; Wu, J. Finetuned YOLOv3 for Getting Four Times the Detection Speed. 2021. DOI: 10.1007/978-3-030-82153-1_42.
  • Zhou, Y.; Chen, C.; Wu, K.; Ning, M.; Chen, H.; Zhang, P. SCTD1.0: Sonar common target detection dataset[J]. Computer Sci. 2021, 48(S2), 334–339. DOI: 10.11896/jsjkx.210100138.
  • Zhao, B.; Lan, H.; Niu, Z.; Zhu, H.; Qian, T.; Tang, W. Detection and Location of Personal Safety Protective Equipment and Workers in Power Substations Using a Wear-enhanced YOLOv3 Algorithm[J]. IEEE. Access. 2021, 9, 1. DOI: 10.1109/ACCESS.2021.3104731.
  • Zhang, Y.; Liu, M.; Xu, S. MobileNetv3-Large-YOLOv3 based substation fire detection[J]. 2021. DOI: 10.3969/j.issn.1007-290X.2021.011.015.
  • Wang, H.; Zhang, F.; Liu, X.; Li, Q. Fruit image recognition based on DarkNet-53 and YOLOv3[J]. J. Northeast Normal Univ. (Nat. Sci. Edition). 2020, 52(4), 6. DOI: 10.16163/j.cnki.22-1123/n.2020.04.010.
  • Joseph, R.; Ali, F. YOLOv3: An Incremental Improvement. Univ. Washington: Computer Vision and Pattern Recog. 2018, 4. DOI: 10.48550/arXiv.1804.02767.
  • Zu, L.; Zhao, Y.; Wang, G.; Liu, P.; Yan, Y.; Zu, L. Tomato Maturity Classification Based on SE-YOLOv3-MobileNetV1 Network under Nature Greenhouse Environment[J]. Agronomy. 2022, 2022(7), 12. DOI: 10.3390/agronomy120716358.
  • Jin, R.; Xu, Y.; Xue, W. An Improved Mobilenetv3-Yolov5 Infrared Target Detection Algorithm Based on Attention Distillation[C]. Springer Cham. 2022. DOI: 10.1007/978-3-030-94551-0_22.
  • Deng, T.; Wu, Y. Simultaneous vehicle and lane detection via MobileNetV3 in car following scene[J]. PLoS One. 2022, 2022(3), 17. DOI: 10.1371/journal.pone.0264551.