객체 탐지를 활용한 근로자 충돌 안전관리 시스템 = Worker Collision Safety Management System using Object Detection

다국어 초록 (Multilingual Abstract)

Recently, AI, big data, and IoT technologies are being used in various solutions such as fire detection and gas or dangerous substance detection for safety accident prevention. According to the status of occupational accidents published by the Ministry of Employment and Labor in 2021, the accident rate, the number of injured, and the number of deaths have increased compared to 2020. In this paper, referring to the dataset construction guidelines provided by the National Intelligence Service Agency(NIA), the dataset is directly collected from the field and learned with YOLOv4 to propose a collision risk object detection system through object detection. The accuracy of the dangerous situation rule violation was 88% indoors and 92% outdoors. Through this system, it is thought that it will be possible to analyze safety accidents that occur in industrial sites in advance and use them to intelligent platforms research.

참고문헌 (Reference)

1 양승의 ; 이성옥 ; 정회경, "전기화재 예측 및 예방을 위한 IoT 플랫폼 시스템" 한국정보통신학회 26 (26): 223-229, 2022

2 A. Bochkovskiy, "YOLOv4: optimal speed and accuracy of object detection"

3 P. Khandelwal, "Using Computer Vision to enhance Safety of Workforce in Manufacturing in Post COVID World"

4 "Severe Accident Penalty Act. Pub. L. No. 17907. § 2"

5 Korea Ministry of Employment and Labor, "Policy archives -occupational accident status in 2021"

6 황철현 ; 김호성 ; 정회경, "Detection and Correction Method of Erroneous Data Using Quantile Pattern and LSTM" 한국정보통신학회 16 (16): 242-247, 2018

7 Korea National Information Society, "AI dataset building guide and quality guide"

8 R. Padilla, "A Survey on Performance Metrics for Object-Detection Algorithms" 237-242, 2020