Vision on the Move: Automated Hazardous Material Plate Detection in Freight Transport

Melissa Tijink, Stanislav Levendeev, Ewaldo Nieuwenhuis, Luuk J. Spreeuwers, Nicola Strisciuglio, Estefanía Talavera

Abstract: Enhancing the logistic efficiency and safety of freight transport requires fast, reliable identification of hazardous materials (hazmat). In this work, we explore how computer vision can automate the detection and reading of hazmat number plates on freight trains and trucks. We benchmark two object detection models for hazmat localization, YOLOv11x and Faster R-CNN, across a private freight train dataset and HazTruck, our newly introduced public dataset. For reading the detected plates, we evaluated three Optical Character Recognition (OCR) methods: the widely used Tesseract, EasyOCR, and the recent vision-language model Idefics2. Integrating YOLOv11x and Idefics2 into a unified pipeline achieved the state-of-the-art performance, with over 90% accuracy on freight train data, showcasing a powerful and scalable solution for automated hazmat identification in transport logistics. The code and datasets are available via https://github.com/Robust-Rail.

External IDs:dblp:conf/caip/TijinkLNSST25