摘要
体长作为鱼类主要可测量属性之一,是其生长状况监测、水质环境调控、饵料投喂、经济效益估算的重要信息依据。近年来,随着成像技术、计算能力和硬件设备的快速发展,基于机器视觉的无损测量方法迅速兴起,克服了传统方法在鱼体损伤、成本和性能方面的局限性,凭借快速准确、及时高效、可重复批量检测的优势成为鱼体长度测量的有力工具。通过文献整理和分析,对基于机器视觉的鱼体长度测量中所需的图像采集设备、鱼体轮廓提取算法和长度测量方法进行了系统的分析和总结,并对不同方法的优缺点和适用场景进行了比较。最后,提出了鱼体长度估算研究的主要挑战和未来趋势。
As one of the visual attributes of fish appearance,body length is a key factor related to the monitoring of fish growth status,regulation of water environment,feeding of bait drugs,quality and safety of fish products and the estimation of economic benefits.However,traditional body length estimation methods involve processes such as capture,anesthesia and manual measurement,which are time-consuming,labor-intensive and low-precision.In addition,it can also cause physiological stress responses and negatively affect the tested fish.With the rapid development of imaging technology,computing power and hardware equipment,non-destructive measurement methods based on machine vision have emerged rapidly,overcoming the limitations of traditional methods in terms of cost and performance.With its advantages of fast,accurate,timely,efficient and repeatable batch detection,it has become a powerful tool for fish body length measurement and plays a positive role in improving the economic benefits of aquaculture.The existing domestic and foreign research literature was summarized and sorted out,and the machine vision-based image acquisition equipment,fish contour extraction algorithms and length measurement methods were systematically analyzed and discussed.High-efficiency image acquisition and high-quality image data were important guarantees for accurate measurement.The advantages,disadvantages and applications of monocular cameras,binocular cameras based on optical imaging were firstly compared and analyzed.Secondly,the extraction of fish body contours from two parts of traditional image processing technology and image segmentation technology based on deep learning was summarized.Then,it was concluded that the underwater fish segmentation method based on deep learning had better robustness and versatility in the complex underwater scene.Using the image acquisition mode as the classification basis,the body length measurement methods based on the 2 D mode and the 3 D mode were described respectively.From the perspective of manual participation,the measurement methods based on the 3 D mode were divided into automation and semi-automation.The semi-automation of stereo intersection methods such as DLT,template matching,and the Haar classifier were summarized.Also,convex hull algorithm,point cloud,and landmark point geometric morphology measurement method based on fully automated three-dimensional measurement methods were listed.However,due to the difficulty of deploying underwater cameras,the complication of underwater scenes,and the sensitiveness of the measured fish body,it was very challenging to apply machine vision technology to the measurement of fish body length widely.At last,the trend of fish body length measurement based on machine vision was proposed.Furthermore,image enhancement was the research focus,and fish contour extraction based on deep learning methods was the key technology.Also,developing length measurements based on 3 D mode was the mainstream method and using three-dimensional point cloud data measurement and geometric features to fit contours was a direction.Machine vision combined with technologies such as deep learning,pattern recognition,and environmental perception,became a key method for obtaining fish growth information,which can provide technical support for the refined and intelligent management of aquaculture.
作者
李振波
赵远洋
杨普
吴宇峰
李一鸣
郭若皓
LI Zhenbo;ZHAO Yuanyang;YANG Pu;WU Yufeng;LI Yiming;GUO Ruohao(College of Information and Electrical Engineering,China Agricultural University,Beijing 100083,China;National Innovation Center for Digital Fishery,China Agricultural University,Beijing 100083,China)
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2021年第S01期207-218,共12页
Transactions of the Chinese Society for Agricultural Machinery
基金
国家重点研发计划项目(2020YFD0900204)
关键词
鱼体长度测量
图像采集
轮廓提取
深度学习
机器视觉
fish body length measurement
image acquisition
contour extraction
deep learning
machine vision
