Won Seok Lee
ABSTRACT
This study is intended to develop an artificial intelligence model capable of recognizing and detecting heavy equipment to compensate for visual observation while driving. The total number of data collected was 6,700 images, of which 5,820 were used as training data and the remaining 880 images as a set of testing data. The YOLOv3 Network proposed in this study shows improved performance compared to the existing YOLOv3 and YOLOv2 with performance indicators for F1-Score 84.65%, Precision 86.94%, and Recall 82.47% in the detection of heavy equipment in the testing data set. Since the heavy equipment detection model proposed in the study is designed to enable real-time detection, it is expected to be helpful in patrolling construction sites in areas where gas pipes are buried.
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