IMAGE MATCHING BASED HAZARDOUS MATERIAL DETECTION AND WARNING SYSTEM

Transportation of dangerous goods involves many critical situations that require safety and special precautions. In accordance with the regulations, hazardous materials, which include international standards, should be closely monitored and precautions should be taken in advance according to the situation. Artificial intelligence, image processing and data analysis techniques can be used to recognize and classify the labels of dangerous goods. This is important for early action in case of an emergency. If hazardous materials are not properly stored or transported according to safety precautions and rules, they can cause both material and moral damage. In this study, a hazardous material detection and warning system using AKAZE, ORB and SIFT image feature matching techniques is developed. To test the system, a dataset of multiple hazardous material labels with different scenes and conditions was created. The performances of feature matching techniques including image processing algorithms are examined through comparative analysis. As a result of image matching, label-related features and intervention information were retrieved from the database and displayed on the system interface. Experimental results show that the ORB technique is the best method for feature matching and accurate matching, and the AKAZE technique is the fastest feature detection method.