Objects Detection Based on Deep Learning with A Convolutional Neural Network Architecture

• Main Authors: Chen, Huan-Chieh, 陳桓杰
• Other Authors: Tsai, Cheng-Fa
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/8m537p

碩士 === 國立屏東科技大學 === 資訊管理系所 === 106 === In recent years, deep learning is popular research area. Deep learning technology is applied to image object recognition, language recognition, medical disease identification, and so forth. This technology can assist humans to work and even replace manual work. Nowadays, convolution neural network is a framework for deep learning. Many researchers are working to build models of convolution neural network. Using corresponding models to extract features for object. Deep learning enables machines to recognize objects as humans and create more possibilities. In this research, we use the Tensorflow Object Detection API to build the models of real-time object recognition. To learn how to build efficient models and compare them. Our datasets are downloaded from ImageNet. Finally, the results of MobileNet, Inception v2, and Inception v3 are compared. In testing 10 classes datasets, Inception v2 is the best among three of them.