GÖRÜNTÜ EŞLEŞTİRME TABANLI TEHLİKELİ MADDE TESPİT VE UYARI SİSTEMİ

Year 2024, Volume: 23 Issue: 46, 271 - 291, 27.12.2024

Fatma Betül Okur Can Eyüpoğlu

https://doi.org/10.55071/ticaretfbd.1469991

Abstract

Tehlikeli maddelerin taşınması güvenlik ve özel önlemler gerektiren birçok kritik durumu içermektedir. Mevzuatlar gereğince uluslararası standartları içeren tehlikeli maddeler yakından takip edilmeli ve duruma göre önceden önlemler alınmalıdır. Yapay zeka, görüntü işleme ve veri analizi teknikleri, tehlikeli maddelerin etiketlerini tanıma ve sınıflandırma konusunda kullanılabilmektedir. Bu durum acil müdahale anında erken hareket etmek için önemlidir. Eğer tehlikeli maddeler güvenlik önlemlerine ve kurallarına göre uygun depolanmazsa veya taşınmazsa hem maddi hem de manevi zarara yol açabilmektedir. Bu çalışmada AKAZE, ORB ve SIFT görüntü özellik eşleştirme tekniklerini kullanan tehlikeli madde tespit ve uyarı sistemi geliştirilmiştir. Sistemi test etmek için farklı sahneleri ve koşulları içeren birden fazla tehlikeli madde etiketinden elde edilen bir veri seti oluşturulmuştur. Karşılaştırmalı analizler ile görüntü işleme algoritmalarını içeren özellik eşleştirme tekniklerinin performansları incelenmiştir. Görüntü eşleştirmesi sonucunda veri tabanından, etiketle ilgili özellikler ve müdahale bilgileri alınarak sistemin arayüzünde görüntülenmesi sağlanmıştır. Deneysel sonuçlar ORB tekniğinin özellik eşleştirmesi ve doğru eşleme konusunda en iyi yöntem olduğunu ve AKAZE tekniğinin en hızlı özellik bulan yöntem olduğunu göstermektedir.

Keywords

Görüntü işleme, AKAZE, ORB, SIFT, tehlikeli maddeler, görüntü özellik eşleştirme teknikleri

IMAGE MATCHING BASED HAZARDOUS MATERIAL DETECTION AND WARNING SYSTEM

Abstract

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.

Keywords

Image processing, image matching techniques, AKAZE, ORB, SIFT, hazardous materials

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