基于线激光扫描的鱼尾切割位置识别方法研究
Research on Fishtail Cutting Position Recognition Method Based on Line Laser Scan-ning
DOI: 10.12677/JSTA.2022.102025, PDF,    科研立项经费支持
作者: 马骏骁, 康家铭, 龚 泽, 张 旭*：大连工业大学机械工程及自动化学院，辽宁 大连；李 刚：济南好为尔机械设备有限公司，山东 济南
关键词: 线激光扫描；位置识别；预测模型；鱼尾切割； Linear Laser Scanning； Location Recognition； Prediction Model； Fishtail Cutting
摘要: 鱼尾切割是鱼类食材分切加工的重要工序之一，机械式切割的合格率和得肉率低，无法满足大规模生产加工的需求。为实现鱼尾自动化精准切割，本文提出一种基于线激光扫描的鱼尾切割位置快速识别方法。利用线激光扫描传感器采集的鱼体表面信息进行鱼体外轮廓点云数据曲线拟合，建立鱼体宽度随长度变化函数，计算出最大宽–长函数导数为0点的位置，将其作为鱼尾切割位置，结果显示全体样本的相对误差值均小于5%，相对标准偏差(RSD)值为2.265%。研究结果可为开发鱼类智能切割设备提供理论参考。
Abstract: Fishtail cutting is one of the most important processes of fish food material cutting. Mechanical cutting pass rate and meat rate are lower, which cannot meet the requirements of large-scale production lines. In order to realize the automatic and precise cutting of fishtails. This paper presents a fast recognition method of fishtail cutting position based on line laser scanning. Using the fish surface information collected by the line laser scanning sensor, curve fitting of fish contour point cloud data was carried out, and establish the function of fish width with length, calculate the position where the derivative of the maximum width-length function is 0 and take it as the fishtail cutting position. The result showed that the relative errors of all samples were less than 5% and Relative Standard Deviation (RSD) value was 2.265%. The research results can provide theoretical reference for the development of integrated intelligent processing equipment.
文章引用：马骏骁, 康家铭, 龚泽, 李刚, 张旭. 基于线激光扫描的鱼尾切割位置识别方法研究[J]. 传感器技术与应用, 2022, 10(2): 202-210. https://doi.org/10.12677/JSTA.2022.102025

参考文献

[1] Li, J., Lu, H., Zhu, J., Wang, Y. and Li, X. (2009) Aquatic Products Processing Industry in China: Challenges and Out-look. Trends in Food Science & Technology, 20, 73-77. [Google Scholar] [CrossRef] 
[2] 王亚楠. 切割损伤对冷鲜草鱼制品贮藏品质影响研究[D]: [硕士学位论文]. 武汉: 武汉轻工大学, 2015.
[3] 陈庆余, 沈建, 傅润泽, 谈佳玉, 张敬峰. 典型海产小杂鱼机械去头方法研究[J]. 渔业现代化, 2012, 39(5): 38-42. [Google Scholar] [CrossRef] 
[4] Wang, H., Zhang, X., Li, P., Sun, J. and Liu, Y. (2020) A New Approach for Unqualified Salted Sea Cucumber Identification: Integration of Image Texture and Machine Learn-ing under the Pressure Contact. Journal of Sensors, No. 4, 1-13. [Google Scholar] [CrossRef] 
[5] Mustafa, A., Volino, M., Kim, H., Guillemaut, J.Y. and Hilton, A. (2020) Temporally Coherent General Dynamic Scene Reconstruction. International Journal of Computer Vision, 129, 123-141. [Google Scholar] [CrossRef] 
[6] Wang, J., Yang, Y. and Zhou, Y. (2021) Dynamic Three-Dimensional Surface Reconstruction Approach for Continuously Deformed Objects. IEEE Photonics Journal, 13, Article ID: 6800415. [Google Scholar] [CrossRef] 
[7] Zhang, C. (2020) Binocular Vision Navigation Method of Ma-rine Garbage Cleaning Robot in Unknown Dynamic Scene. Journal of Coastal Research, 103, 864-867. [Google Scholar] [CrossRef] 
[8] Liu, X., Chen, B., He, Y. and Li, D. (2020) Development of an Autono-mous Object Transfer System by an Unmanned Aerial Vehicle Based on Binocular Vision. International Journal of Ad-vanced Robotic Systems, 17, 1-17. [Google Scholar] [CrossRef] 
[9] Zhi, L., Xiang, C.Q. and Chen, T. (2018) Automated Binocular Vision Measurement of Food Dimensions and Volume for Dietary Evaluation. Computing in Science and Engineering, No. 99, 1. [Google Scholar] [CrossRef] 
[10] Antequera, T., et al. (2020) Evaluation of Fresh Meat Quality by Hyperspectral Imaging (HSI), Nuclear Magnetic Resonance (NMR) and Magnetic Resonance Imaging (MRI): A Re-view. Meat Science, 172, Article ID: 108340. [Google Scholar] [CrossRef] [PubMed]
[11] Ando, M., Sugiyama, H., Maksimenko, A., Rubenstein, E., Roberson, J., Shimao, D., et al. (2004) X-Ray Dark-Field Imaging and Its Application to Medicin. Radiation Physics & Chemistry, 71, 899-904. [Google Scholar] [CrossRef] 
[12] 初梦苑, 刘刚, 司永胜, 冯凡. 基于三维重建的奶牛体重预估方法[J]. 农业机械学报, 2020, 51(S1): 385-391.
[13] Tsoulias, N., Xanthopoulos, G.G., Fountas, S. and Zude-Sasse, M. (2020) In-Situ Detection of Apple Fruit Using a 2D LiDAR Laser Scanner. IEEE International Work-shop on Metrology for Agriculture and Forestry (MetroAgriFor), Trento, 4-6 November 2020, 278-282. [Google Scholar] [CrossRef] 
[14] Chen, H., Liu, Z., Jie, G., Wu, A., Wen, J. and Cai, K. (2018) Quantitative Analysis of Soil Nutrition Based on FT-NIR Spectroscopy Integrated with BP Neural Deep Learning. Analytical Methods, 10, 5004-5013. [Google Scholar] [CrossRef] 
[15] Cobourn, W.G., Dolcine, L., French, M. and Hubbard, M.C. (2000) A Comparison of Nonlinear Regression and Neural Network Models for Ground-Level Ozone Forecasting]. Air Repair, 50, 1999-2009. [Google Scholar] [CrossRef] [PubMed]
[16] 蔡原, 刘哲, 宋明伟, 李霁昕, 蒋玉梅. 虹鳟不同部位鱼肉挥发性风味物质组成比较[J]. 食品科学, 2011, 32(16): 269-273.
[17] 徐淑婷. 线激光在机测量关键技术研究[D]: [硕士学位论文]. 大连: 大连理工大学, 2017.
[18] Jia, J., et al. (2019) Calibration Curve and Support Vector Regression Methods Applied for Quantification of Cement Raw Meal Using Laser-Induced Breakdown Spectroscopy. Plasma Science and Technology, 21, Article ID: 034003. [Google Scholar] [CrossRef] 
[19] 丁岚, 谢孟峡, 刘媛, 等. 高效液相色谱法测定鸡蛋中呋喃唑酮的残留量[J]. 分析化学, 2004, 32(2): 139-142.
[20] 许林云, 韩元顺, 陈青, 等. Data-SSI与图论聚类结合识别果树固有频率[J]. 农业工程学报, 2021, 37(15): 136-145.
[21] 胡力, 王芳梅, 吕明珊, 等. 不同贮藏温度下真空包装鸡肉酱品质变化及货架期模型的建立[J]. 食品与发酵工业, 2021, 47(10): 132-138.
[22] Yarahmadi, N. and Vatankhah, A.R. (2021) Experimental Study on Rectangular Cut-Throated Flume: Effects of Flume Walls Slopes and Channel Longitudinal Slope. Flow Measurement and Instrumentation, 79, Article ID: 101919. [Google Scholar] [CrossRef] 

为你推荐