Classification of the Loading Type of Trucks using Convolutional Neural Network
Dong Gyu Lee
Dong Gyu Lee, Professor, Division of Information Technology, Convergence, Shinhan University, South Korea.
Manuscript received on 05 March 2019 | Revised Manuscript received on 12 March 2019 | Manuscript Published on 20 March 2019 | PP: 79-82 | Volume-8 Issue- 4S2 March 2019 | Retrieval Number: D1S0018028419/2019©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: This paper proposes a classification method using Convolutional Neural Network(CNN) to classify the types of a truck. The images of the vehicle from the camera are classified according to the vehicle type and the cargo compartment. Those data are used as training data. To training the neural networks with supervised learning, the appropriate CNN structure is designed and classified images and correct output results are presented to train the weights of neural networks. When the actual image is input, the output of CNN can be used to distinguish whether the loading part of a truck is the covered or not. Experimental results show that images can be classified according to car type and loading type of cargo and it can be used for the pre-classification of loading defect inspection.
Keywords: Convolutional Neural Network, Classification of Vehicle Type, Type of Trucks.
Scope of the Article: Classification