Underwater Image Based Fish Detection and Recognition Using Deep Learning Algorithm
作者单位：华南理工大学
学位级别：硕士
导师姓名：张鑫
授予年度：2019年
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 090803[农学-渔业资源] 0908[农学-水产] 081104[工学-模式识别与智能系统] 08[工学] 080203[工学-机械设计及理论] 09[农学] 0835[工学-软件工程] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
摘 要：The underwater object detection is important for monitoring the sustainability of the marine ***,the conventional monitoring system requires lots of resources because a marine ecosystem is difficult to ***,the computer vision based underwater object detection can be applied to solve the *** thesis aims to detect the fish and recognize its type at the same *** challenges emerged in detecting and recognizing the fish type,such as poor image quality（unclear,blurry and small images resolution）,the similar fish shape,the movement of fish in the water,and the limited fish *** propose to use deep learning algorithms,like YOLO and Faster R-CNN,for detecting and recognizing the fish *** this is the first time YOLO version 3 algorithm applied for fish detection and ***53 and VGG16 are applied to YOLO version 3 and Faster R-CNN respectively as the feature *** datasets were used including fish4 knowledge and bubble *** fish4 knowledge dataset set is a public data set that has several neatly arranged images from each class,while the bubble vision dataset consists of videos that must be preprocessed before being used in the *** order to get the quality image in the dataset,image preprocessing is applied using image enhancement methods including Image Filter（Homomorphic Filter and Gaussian Filter）,Contrast adaptive histogram equalization（CLAHE）,Multifision（CLAHE + Image filter）,Dark Channel *** experiment results showed the best result for fish detection and recognition obtained the performance of 99.16% using Faster R-CNN with multifision（CLAHE + Image filter）image enhancement on Bubble Vision *** YOLO version 3 only performed 74% without image enhancement.
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