基于稠密光流和统计学的虾苗活力检测方法

Detection method of shrimp vitality based on dense optical flow and statistics

目前虾苗活力检测主要是依赖人工观测虾苗的游动情况,评价结果容易受主观影响。本研究提出了一种结合稠密光流法和统计学的虾苗活力检测方法。首先通过在不同水温中饲养虾苗得到不同活力的虾苗,采集虾苗视频;然后使用Gunner Farneback光流法提取虾苗视频中虾苗的帧间运动特征,即虾苗瞬时的游动状态,包含幅值强度和相角角度两个维度,同时对其进行统计学分析;最后提取固定时长虾苗视频的所有帧间运动特征,并应用改进后的信息熵(Weighted information entropy-WIE)和互信息(Mutual information-MI)2种方法对虾苗进行活力检测。结果显示:2种检测方法的精准度和召回率均超过了98%,可以准确客观地实现虾苗活力检测。本研究可以为自动化虾苗质量检测提供思路和方法,在水产养殖行业中有应用价值。

Currently,shrimp fry vitality detection mainly relies on manual observation of shrimp fry swimming,but the evaluation results are susceptible to subjective influence.To address this problem,this study proposes a shrimp fry vitality detection method that combines the dense optical flow method and statistics.Firstly,shrimp fry with different vitality were obtained by rearing them in different water temperatures,and shrimp fry videos were collected;then the inter-frame motion features of shrimp fry in the videos were extracted using the Gunner Farneback optical flow method,which represents the instantaneous swimming state of the shrimp fry,and contains two dimensions,namely,magnitude and angle,and statistical analyses were performed at the same time;finally,we extracted the motion features between all the frames of the fixed-length shrimp video and applied the improved information entropy and mutual information to detect the vitality of the shrimp fry.After testing,the precision and recall of the two detection methods exceeded 98%,which can accurately and objectively realize the shrimp fry vitality detection.This study provides ideas and methods for automated shrimp fry quality detection,which has application value in aquaculture industry.