AUTOMATIC DETECTION OF FREE LIME USING DEEP LEARNING AND ASSESSMENT OF BRIDGE INSPECTOR FOR OUTPUT RESULTS SIZE

Abstract

Inspection methods using new technologies such as deep learning have recently attracted attention as an alternative method of previous close visual inspection of bridges. New technologies which automatically detect damage from the image recognition of the bridge support bridge inspectors to diagnosie the damage, and the inspection work will be lobersaving. However, it is necessary to have the detection display accuracy which can make the decision equivalent to close visual inspection. In this study, free lime which is one of inspection items of the bridge inspection was automatically detected by the image recognition, and we verified the accuracy of the display result necessary for the damage decision of the engineer. In addition, we propose an appropriate display method for automatic detection of free lime as diagnostic information for bridge diagnosticians.