TRUDI and TITUS: A Multi-Perspective Dataset and A Three-Stage Recognition System for Transportation Unit Identification

TL;DR

This paper introduces TRUDI, a large annotated dataset of transportation units in port environments, and TITUS, a three-stage system for accurately recognizing unit IDs across diverse conditions, advancing port logistics automation.

Contribution

The paper provides the first comprehensive, publicly available dataset for transportation unit recognition and a robust three-stage recognition system adaptable to various port scenarios.

Findings

TITUS achieves high accuracy in diverse lighting and weather conditions.

TRUDI dataset contains over 35,000 annotated instances across five categories.

The system outperforms existing methods in multi-perspective port environments.

Abstract

Identifying transportation units (TUs) is essential for improving the efficiency of port logistics. However, progress in this field has been hindered by the lack of publicly available benchmark datasets that capture the diversity and dynamics of real-world port environments. To address this gap, we present the TRUDI dataset-a comprehensive collection comprising 35,034 annotated instances across five categories: container, tank container, trailer, ID text, and logo. The images were captured at operational ports using both ground-based and aerial cameras, under a wide variety of lighting and weather conditions. For the identification of TUs-which involves reading the 11-digit alphanumeric ID typically painted on each unit-we introduce TITUS, a dedicated pipeline that operates in three stages: (1) segmenting the TU instances, (2) detecting the location of the ID text, and (3) recognising and validating the extracted ID. Unlike alternative systems, which often require similar scenes, specific camera angles or gate setups, our evaluation demonstrates that TITUS reliably identifies TUs from a range of camera perspectives and in varying lighting and weather conditions. By making the TRUDI dataset publicly available, we provide a robust benchmark that enables the development and comparison of new approaches. This contribution supports digital transformation efforts in multipurpose ports and helps to increase the efficiency of entire logistics chains.