植基於深度學習於辨識物體之卷積神經網路架構

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

陳桓杰(Chen Huan-Chieh)

指導教授 ： 蔡正發

國立屏東科技大學/管理學院/資訊管理系所/碩士(2018年)

https://doi.org/10.6346/THE.NPUST.MIS.023.2018.F08

摘要

近年來深度學習是熱門研究領域，常用於影像物體辨識、語言辨識、醫療疾病辨識等，輔助人類進行作業，甚至能夠取代人力作業，達到完全自動化生產。卷積神經網路是目前深度學習一種框架，有許多研究提出網路模型，針對物體使用對應的模型提取特徵，藉由深度學習使機器如同人類辨識事物，創造更多可能性。
本研究利用Object Detection API建置即時物體辨識的模型，學習建置表現良好模型與進行觀察比較，由ImageNet下載的小型資料集進行訓練與測試，最後比較MobileNet、Inception v2、Inception v3結果，發現在10類資料集裡，以Inception v2表現較優良。

關鍵字

卷積神經網路 ； 深度學習 ； 影像辨識

並列摘要

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.

並列關鍵字

convolutional neural network ； deep learning ； image recognition

參考文獻

參考文獻

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