• Proceedings of the Korean Institute of Building Construction Conference
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• 2015.11a
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• pp.87-88
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• 2015

A defect dispute surrounding an apartment building is in constant trouble, a defect consulting is necessary that objective and impartial for solving a defect. The defect dispute about a defect lawsuit is conducted in the order that filling of lawsuit, consulting order, defect inspecting, estimating a repairing cost, writing a consulting report, submitting a consulting report, and decision by court. Of these, a step of defect inspecting is extensively investigated an occurred defect that each defect index and type from each part and place. At this time, it is collected of many data and created many information. For this, it need to organize and manage. The study is a modeling of field investigation work process that second phase of defect inspecting step. A literature study is defined a work until level 2. This study is defined the work until level 3 to 4. In addition, the modeling can do for using a job name, a place to job, a job to do, and a person concerned about defect consulting case. The modeling is expected a contribution of improving a defect consulting process and systematizing a judgment standard.

• Journal of Convergence for Information Technology
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• v.8 no.6
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• pp.375-380
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• 2018

This paper proposes a classification method using the Convolutional Neural Network(CNN) which can obtain the type of trucks from the input image without the feature extraction step. To automatically classify vehicle images according to the type of truck cargo box, the top view images of the vehicle are used as input image and we design the structure of the CNN suitable for the input images. Learning images and correct output results is generated and the weights of neural network are obtained through the learning process. The actual image is input to the CNN and the output of the CNN is calculated. The classification performance is evaluated through comparison CNN output with actual vehicle types. Experimental results show that vehicle images could be classified with more than 90 percent accuracy according to the type of cargo box and this method can be used for pre-classification for inspecting loading defect.