DEVELOPMENT OF AI-BASED CONSTRUCTION MACHINE DETECTING SYSTEM AND EXAMINATION ON ACCURACY IMPROVEMENT

Abstract

The development of AI technologies that can derive information on construction management from onsite images has been developed very actively in the decade. We developed an AI-based system to detect construction machines from the site images automatically for site performance management. As a result of operating this system, the detection error was ±14%. To realize highly accurate detection, the amount of learning data for AI and the bias of the orientation of construction machines have significant influence. In this paper, we focused on the number of labels and direction of construction machines and examined the effect of learning data on detection error. As a result, we showed that it is possible to improve the detection accuracy with less learning data by creating learning data by setting a certain rule instead of increasing the learning data randomly.