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《Computer Applications and Software》 2019-02
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Li Ning;Wang Yuxuan;Xu Shoukun;Shi Lin;School of Information Science and Engineering,School of Mathematics and Physics,Changzhou University;Fujian Key Laboratory of Information Processing and Intelligent Control (Minjiang College);  
In the recognition of floating objects on water surface,the small amount of image data and the influence of noise leads to the low recognition accuracy.In order to solve this problem,we adopted floating objects on water surface recognition method with small sample based on deep learning to identify common pollutants,plastic bags and plastic bottles.The convolution neural network model AlexNet was constructed and trained from the images of plastic bags and bottles in the existing large data sets.The gradient descent method was used to fine-tune the model,and the fusion light correction method was used to process the image to be recognized.The network recognition results were compared with the traditional HOG feature extraction methods.The experimental results show that compared with the traditional feature extraction method,the method improves the recognition rate of floating objects on the water surface by nearly 15%.
【Fund】: 闽江学院福建省信息处理与智能控制重点实验室开放课题(MJUKF201740)
【CateGory Index】: TP391.41;TP183
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