Design and Implementation of Data Preprocessing for Deep Learning with Image Recognition

• Main Authors: LIN,CHUN-CHENG, 林俊丞
• Other Authors: HUANG,KO-WEI
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/592q48

碩士 === 國立高雄科技大學 === 電機工程系 === 107 === In the past, the success or failure of traditional machine learning to identify images is often inextricably linked to the extraction of feature values. The tremendous increase in equipment and computing power has given depth learning an opportunity to emerge, and its powerful automatic extraction feature capability can effectively find good features to improve the performance of the model recognition system. The purpose of this study is to apply a variety of binarization pre-processing algorithms, and to implement an image recognition system through the deep learning based convolutional neural network module GoogLeNet. We will use ImageNet, the world's largest image recognition database, and select five of them includes cars, airplanes, horses, boats and trucks as our training input data. The system attempts to grayscale and binarize the image using a variety of algorithms, and then uses GoogLeNet's Inception classification V1, Inception classification V2 and Inception classification V3 to train and try to improve the original model identification results accuracy. It will be implemented in two parts. First, the image pre-processing part, the image will be pre-processed using six different methods of selecting the threshold function provided by OpenCV and the Otsu algorithm. Second, using three different versions models to train and test the data that have already been image pre-processing. Through adjusting and updating the parameters to analyze and compare the results of the experiment. Finally we will use TensorBoard to observe the training process. Various visual data help us to understand and adjust the model to find out the optimal network model, and show that the image pre-processed image can effectively improve the correct rate of model identification.