An Effective Deep-Learning Training Method using Data Augmentation
Changhyung Kim1, Deayol Kim2, Sooyoung Cho3, Chanil Park4, Kyounghak Lee5
1Changhyung Kim, Department of Electronics and Communications Engineering, Kwangwoon University, Seoul (Korea), East Asian.
2Deayol Kim, Department of Electronics and Communications Engineering, Kwangwoon University, Seoul (Korea), East Asian.
3Sooyoung Cho, Department of Electronics and Communications Engineering, Kwangwoon University, Seoul (Korea), East Asian.
4Chanil Park, Department of Electronics and Communications Engineering, Kwangwoon University, Seoul (Korea), East Asian.
5Kyounghak Lee, International Antiques & Collectors Fairs, Kwangwoon University, Seoul (Korea), East Asian.
Manuscript received on 20 June 2019 | Revised Manuscript received on 27 June 2019 | Manuscript Published on 22 June 2019 | PP: 328-331 | Volume-8 Issue-8S2 June 2019 | Retrieval Number: H10600688S219/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Deep learning is changing the research paradigm, showing dramatic performance improvements in many areas of computer vision. Methods/Statistical analysis: It should be <70 words. Since Lecun’s Lenet was released, Deep Learning has achieved significant performance improvements in object recognition and classification. However, it takes a huge data and takes a long time in order to learn, making it difficult to apply to real industrial environments. This method requires many manpower, high know-how and a lot of development time. Therefore, we propose an effective deep-training training and performance enhancement method using data augmentation. After changing the original image to a YUV color space favorable to computer vision, the image is created by raising or lowering the luminance value in units of 5.Using the proposed data augmentation method can save time and cost. Findings: In order to achieve satisfactory performance by applying deep learning to a real industrial environment, we must use our own method of producing a huge amount of data. In addition, the method of producing a direct dataset requires collecting a large amount of image data sets for a specific object and sorting the data with high quality. In this paper, we propose efficient learning method of SSD (Single Shot MultiBox Detector)deepening learning image object recognition model based on MobileNet which is widely used in a mobile environment and embedded environment and data augmentation method to improve recognition performance (mAP). Improvements/Applications: In SSDbased on MobileNet, the saturation of loss is faster than that of the original data set alone, and the mAP is improved by 0.7.
Keywords: Deep-Leaning, SSD, Mobilenet, Data Augmentation, Luminance Variation.
Scope of the Article: Communications