Sea Cucumber Identification Fishing System Based on Hog Super Pixel Feature Extraction

Abstract

Due to the refraction, reflection and absorption of light induced by seawater and suspended particles, the image recognition of underwater vehicle has the problem of low accuracy and difficult localization, and to solve this one problem, an improved LSTM classification algorithm (CH-LSTM) based on CLAHE and HOG is given. First, the improved CLAHE algorithm is used to enhance image by image noise removal and de-fogging, the problems of underwater image distortion, fine lines, and abrupt changes are solved in the aquaculture waters image. Then, the Histogram of Orientation Gradient (HOG) operator is used for feature extraction to describe the shape of sea cucumbers and generate feature vectors; finally, the LSTM algorithm is used to classify the feature vectors and avoid overfitting through memory gates, so the generalization ability of the model was enhanced. The identification and sorting experiments of underwater fishing sea cucumbers show that the proposed algorithm is superior, especially in the environment of poor water quality, and the identification and localization accuracy of sea cucumbers is improved compared with the traditional ground SVM and BP neural network algorithms, and the identification accuracy is above 95.28%.