基于改进主成分分析和AdaBoost算法的运动虾苗识别方法

针对虾行为量化过程中运动虾苗较难检测与识别的问题,该文以南美白对虾虾苗为例,提出了一种基于改进主成分分析(principal component analysis,PCA)+AdaBoost算法的运动虾苗自动识别方法。在室内自然光条件下,利用工业相机采集承装容器中虾苗的灰度图像。提取图像中大小为100×100像素的不同运动状态的虾苗图像,首先使用改进PCA算法进行主成分分析,并进行特征提取。根据特征参数的分布情况,对其进行归一化处理,利用归一化的特征构建多个弱分类器,利用Adaboost方法将弱分类器构建成强分类器。最后,利用强分类器对运动虾苗进行识别。试验结果表明,在150幅不同运动状态虾苗测试样本中,基于改进PCA+Adaboost方法的识别正确率98%,平均每个样本识别时间为0.027 898 s,满足行为量化中的自动识别要求。

Moving larval shrimps recognition based on improved principal component analysis and AdaBoost

Abstract: Recognition of larval shrimp is the key to realize the quantification of the behavior of larval shrimp. Recently, the studies on aquatic animal behavior recognition mainly use the typical features method or frame differential method. It is hard to accurately identify larval shrimp due to the lack of key profile information caused by the strong motion distortion and background noise. To solve this problem, we took the Penaeus vannamei as research object, and proposed a recognition method for moving larval shrimp based on the improved PCA (principal component analysis) and Adaboost algorithm in this paper. Firstly, we extracted and optimized the effective features. The improved PCA algorithm was applied to the larval shrimp images with the size of 100 × 100 pixels to reduce the image dimension and extract the external features. We then analyzed and determined the number of principal components according to the distribution of the contribution rate. After that we expounded the theory of the Adaboost algorithm, and used these principal components to build different weak classifiers. A total of 100 images of larval shrimp and background samples were used for training. During the training every weak classifier was selected automatically, and a stage classifier was generated after 20 iterations. To verify the effectiveness of the proposed algorithm, the recognition experiment was performed with 150 sample images containing 100 images of larval shrimp and 50 sample images of background. The images were captured in the Key Laboratory of Fishery Equipment and Engineering Technology of Ministry of Agriculture in Shanghai, China. The experiment results showed that, the recognition rate for stationary and slowly-moving larval shrimp and fast-moving larval shrimp was 100% and 96% respectively, and the overall recognition rate reached 98%. Among all the test samples, the recognition time for each image ranged from 25 to 35 ms, and the average time was 27.898 ms. Compared with the conventional typical feature method and the frame differential method, the proposed method can be used to recognize the moving larval shrimp. Furthermore, the proposed method has better performance, because it effectively restrains the influence of complex situation such as the velocity and direction of motion and the sample''s size. This paper provides a technical basis for the larval shrimp''s behavior quantification.