Dangerous Goods Detection and Warning Approach Based on Image Processing Techniques

Fatma Betül Okur; Can Eyüpoğlu

Article Details

Dangerous Goods Detection and Warning Approach Based on Image Processing Techniques

Authors:

Fatma Betül Okur | Can Eyüpoğlu

Publisher:

Fırat University

Main Topics:

Image Processing | Computer System Software

Journal:

Turkish Journal of Science and Technology Vol.20 Issue 1, pp.235-248

Document Type:

Article

Publication Date:

3/2025

Document Language:

English

Document ID (Org No.):

2001341489

pISSN:

1308-9080

Keywords:

Abstract:

 Hazardous substances are widely used in many sectors such as industry, logistics, agriculture and energy, but they carry potentially serious risks. Accurate identification of these risks before the materials start transportation processes is critical to prevent potential accidents and minimize risks. This study presents an approach to preventing accidents that may occur in the transport of dangerous goods to ensure rapid, effective intervention in case of possible accidents and to take early precautions. Optical Character Recognition OCR technology, one of the image processing techniques, is used in the study. Dangerous goods labels were detected with the help of OCR algorithms and the texts on the label were successfully detected. The detected texts, especially the United Nations UN numbers specific to hazardous substances, were matched with a previously created database. Based on the UN numbers matched with the database, the properties of the relevant substance, response conditions, precautions to be taken and other critical information were retrieved from the database and presented to the users. This information is matched with visual outputs and transferred to the user through warning systems. In the study, a dataset of 600 images containing hazardous material labels with various background conditions was used. In the tests performed on the dataset, the performance of the system was evaluated by calculating accuracy metrics. The results show the effectiveness of the OCR-based approach in detecting and processing hazardous material labels. This study provides an important contribution for safe transportation and rapid response processes, especially in large-scale logistics operations.

 Hazardous substances are widely used in many sectors such as industry, logistics, agriculture and energy, but they carry potentially serious risks. Accurate identification of these risks before the materials start transportation processes is critical to prevent potential accidents and minimize risks. This study presents an approach to preventing accidents that may occur in the transport of dangerous goods to ensure rapid, effective intervention in case of possible accidents and to take early precautions. Optical Character Recognition OCR technology, one of the image processing techniques, is used in the study. Dangerous goods labels were detected with the help of OCR algorithms and the texts on the label were successfully detected. The detected texts, especially the United Nations UN numbers specific to hazardous substances, were matched with a previously created database. Based on the UN numbers matched with the database, the properties of the relevant substance, response conditions, precautions to be taken and other critical information were retrieved from the database and presented to the users. This information is matched with visual outputs and transferred to the user through warning systems. In the study, a dataset of 600 images containing hazardous material labels with various background conditions was used. In the tests performed on the dataset, the performance of the system was evaluated by calculating accuracy metrics. The results show the effectiveness of the OCR-based approach in detecting and processing hazardous material labels. This study provides an important contribution for safe transportation and rapid response processes, especially in large-scale logistics operations.

 Hazardous substances are widely used in many sectors such as industry, logistics, agriculture and energy, but they carry potentially serious risks. Accurate identification of these risks before the materials start transportation processes is critical to prevent potential accidents and minimize risks. This study presents an approach to preventing accidents that may occur in the transport of dangerous goods to ensure rapid, effective intervention in case of possible accidents and to take early precautions. Optical Character Recognition OCR technology, one of the image processing techniques, is used in the study. Dangerous goods labels were detected with the help of OCR algorithms and the texts on the label were successfully detected. The detected texts, especially the United Nations UN numbers specific to hazardous substances, were matched with a previously created database. Based on the UN numbers matched with the database, the properties of the relevant substance, response conditions, precautions to be taken and other critical information were retrieved from the database and presented to the users. This information is matched with visual outputs and transferred to the user through warning systems. In the study, a dataset of 600 images containing hazardous material labels with various background conditions was used. In the tests performed on the dataset, the performance of the system was evaluated by calculating accuracy metrics. The results show the effectiveness of the OCR-based approach in detecting and processing hazardous material labels. This study provides an important contribution for safe transportation and rapid response processes, especially in large-scale logistics operations.

 Tehlikeli maddeler, sanayi, lojistik, tarım ve enerji gibi birçok sektörde yaygın olarak kullanılmakla birlikte, potansiyel olarak ciddi riskler taşımaktadır. Bu risklerin, maddeler taşımacılık süreçlerine başlamadan önce doğru bir şekilde belirlenmesi, olası kazaların önlenmesi ve risklerin minimize edilmesi açısından kritik öneme sahiptir. Bu çalışma, tehlikeli madde taşımacılığında meydana gelebilecek kazaların önlenmesi, olası kaza durumlarında hızlı, etkili müdahale sağlanması ve erken önlem alınmasına yönelik bir yaklaşım sunmaktadır. Çalışmada, görüntü işleme tekniklerinden biri olan Optik Karakter Tanıma Optical Character Recognition-OCR teknolojisi kullanılmıştır. Tehlikeli madde etiketleri OCR algoritmaları yardımıyla tespit edilmiş ve etiket üzerindeki metinler başarılı bir şekilde algılanmıştır. Algılanan metinler, özellikle tehlikeli maddelere özgü Birleşmiş Milletler United Nations-UN numaraları, önceden oluşturulan bir veri tabanı ile eşleştirilmiştir. Veri tabanı ile eşleştirilen UN numaraları üzerinden, ilgili maddeye ait özellikler, müdahale koşulları, alınması gereken önlemler ve diğer kritik bilgiler veri tabanından alınarak kullanıcılara sunulmuştur. Bu bilgiler, görsel çıktılarla eşleştirilerek uyarı sistemleri aracılığıyla kullanıcıya aktarılmıştır. Çalışmada, tehlikeli madde etiketlerini içeren ve çeşitli arka plan koşullarına sahip 600 adet görselden oluşan bir veri seti kullanılmıştır. Veri seti üzerinde yapılan testlerde, doğruluk metrikleri hesaplanarak sistemin performansı değerlendirilmiştir. Elde edilen sonuçlar, OCR tabanlı yaklaşımın tehlikeli madde etiketlerinin algılanması ve işlenmesindeki etkinliğini göstermiştir. Bu çalışma, özellikle büyük ölçekli lojistik operasyonlarında, güvenli taşıma ve hızlı müdahale süreçleri için önemli bir katkı sunmaktadır.

Article Details

Dangerous Goods Detection and Warning Approach Based on Image Processing Techniques

Authors:

Fatma Betül Okur | Can Eyüpoğlu

Publisher:

Fırat University

Main Topics:

Image Processing | Computer System Software

Journal:

Turkish Journal of Science and Technology Vol.20 Issue 1, pp.235-248

Document Type:

Article

Publication Date:

3/2025

Document Language:

English

Document ID (Org No.):

2001341489

pISSN:

1308-9080

Image Processing

Dangerous Goods

Optical Character RecognitionGörüntü işleme

tehlikeli madde

optik karakter tanıma

Issues

Article Details

Dangerous Goods Detection and Warning Approach Based on Image Processing Techniques

Authors: Fatma Betül Okur | Can Eyüpoğlu

Publisher: Fırat University

Main Topics: Image Processing | Computer System Software

Journal: Turkish Journal of Science and Technology Vol.20 Issue 1, pp.235-248

Document Type: Article

Publication Date: 3/2025

Document Language: English

Document ID (Org No.): 2001341489

pISSN: 1308-9080

Image Processing

Dangerous Goods

Optical Character RecognitionGörüntü işleme

tehlikeli madde

optik karakter tanıma

Issues

Authors:

Fatma Betül Okur | Can Eyüpoğlu

Publisher:

Fırat University

Main Topics:

Image Processing | Computer System Software

Journal:

Turkish Journal of Science and Technology Vol.20 Issue 1, pp.235-248

Document Type:

Article

Publication Date:

3/2025

Document Language:

English

Document ID (Org No.):

2001341489

pISSN:

1308-9080