Research on Daily Objects Detection Based on Deep Neural Network

2017 International Symposium on Application of Materials Science and Energy Materials

材料科学;能源学

Ding, Sheng^1 ; Zhao, Kun^2

Key Laboratory of Fiber Optic Sensing Technology and Information Processing, Ministry of Education, Wuhan University of Technology, Wuhan, China^1

Fiberhome Telecommunication Technologies Co., Ltd, Wuhan, China^2

关键词: Daily object;    Data set;    Fine tuning;    Model parameters;    Object detection method;    Small data set;    Training model;    Training process;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/322/6/062024/pdf
DOI  :  10.1088/1757-899X/322/6/062024

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

With the rapid development of deep learning, great breakthroughs have been made in the field of object detection. In this article, the deep learning algorithm is applied to the detection of daily objects, and some progress has been made in this direction. Compared with traditional object detection methods, the daily objects detection method based on deep learning is faster and more accurate. The main research work of this article: 1. collect a small data set of daily objects; 2. in the TensorFlow framework to build different models of object detection, and use this data set training model; 3. the training process and effect of the model are improved by fine-tuning the model parameters.