Gas Cylinder Detection Using Deep Learning Based YOLOv5 Object Detection Method

Abstract

Detection and tracking of objects has critical importance in terms of speeding up the process and facilitating the work in many areas. Especially in the process of counting objects, which is difficult and time-consuming for experts. In this paper, a study was carried out to detect gas cylinders with different colors and shapes using the deep learning-based Yolov5 method. The process of counting cylinders in the stock area or in the filling facilities can be difficult for the specialist due to the different sizes, arrangement and large number of cylinders. Within the scope of the study, a data set containing different types of cylinders in gas filling facilities was created. When the obtained results are evaluated, it has been observed that the Yolov5 algorithm detects the gas cylinders with different color and shape properties with a high success rate of 96.16%. In addition to the detection success, it has been observed that the method is also successful in different objective detections such as precision, sensitivity and box intersection.