석탄 이송 설비에 적용이 가능한 카메라 영상 분석 기반 낙탄 모니터링 방안에 대한 연구

다국어 초록 (Multilingual Abstract)

Currently, power plants are using conveyor belts that are used to transport large amounts of coal. However, there is a risk of fire due to the falling coal that occurs during transport. Currently, human resources are deployed to prevent accidents through patrol inspections, but due to the wide range of transport facilities, there is no way to check the status of droppings occurring in all places. Therefore, in this paper, we proposed a method that can always monitor the height of a drop and provide it by determining the height by using a CNN algorithm that shows high accuracy and performance in recent image analysis. A 1-Stage object detection neural network that can be used for the detection of a drop was selected, and basic performance verification of the study was performed using the inference model generated through retraining. The homework on how to more accurately calculate the area of the drop remains, but it is expected to contribute to automating the monitoring of the drop over a wide range of areas that were performed by putting manpower into the field in the future.

목차 (Table of Contents)

• Abstract
• I. 서론
• II. 관련 연구
• 1. 배경 제거
• 2. 영상처리 기반 객체 검출
• 3. CNN 기반 객체 검출
• III. 본론
• Ⅳ. 구현 및 실험 결과
• V. 결론
• [ 참고문헌 ]