# Automatic Intermodal Loading Unit Identification using Computer Vision: A Scoping Review

arXiv: https://arxiv.org/abs/2509.17707 · HTML: https://mlsanity.com/paper/2509.17707/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 158 references that are in the map.
- First posted on arXiv 22 Sep 2025.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition](https://mlsanity.com/paper/1507.05717/) (2015)
- [Synthetic Data for Text Localisation in Natural Images](https://mlsanity.com/paper/1604.06646/) (2016)
- [Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition](https://mlsanity.com/paper/2103.06495/) (2021)
- [Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes](https://mlsanity.com/paper/1908.08207/) (2019)
- [DiffuMask: Synthesizing Images with Pixel-level Annotations for Semantic Segmentation Using Diffusion Models](https://mlsanity.com/paper/2303.11681/) (2023)
- [Scene Text Detection and Recognition: The Deep Learning Era](https://mlsanity.com/paper/1811.04256/) (2018)
- [Towards End-to-End Unified Scene Text Detection and Layout Analysis](https://mlsanity.com/paper/2203.15143/) (2022)
- [Detecting Curve Text in the Wild: New Dataset and New Solution](https://mlsanity.com/paper/1712.02170/) (2017)
- [Text Spotting Transformers](https://mlsanity.com/paper/2204.01918/) (2022)
- [UnrealText: Synthesizing Realistic Scene Text Images from the Unreal World](https://mlsanity.com/paper/2003.10608/) (2020)
- [A Survey on Security and Privacy Issues of UAVs](https://mlsanity.com/paper/2109.14442/) (2021)
- [Bridging Synthetic and Real Worlds for Pre-training Scene Text Detectors](https://mlsanity.com/paper/2312.05286/) (2023)
- [HCR-Net: A deep learning based script independent handwritten character recognition network](https://mlsanity.com/paper/2108.06663/) (2021)
- [Full or Weak annotations? An adaptive strategy for budget-constrained annotation campaigns](https://mlsanity.com/paper/2303.11678/) (2023)
- [Linguistics-aware Masked Image Modeling for Self-supervised Scene Text Recognition](https://mlsanity.com/paper/2503.18746/) (2025)
- [Deep Learning, Machine Learning -- Digital Signal and Image Processing: From Theory to Application](https://mlsanity.com/paper/2410.20304/) (2024)
- [Automatic Number Plate Recognition using Random Forest Classifier](https://mlsanity.com/paper/2303.14856/) (2023)
- [Small Objects Matters in Weakly-supervised Semantic Segmentation](https://mlsanity.com/paper/2309.14117/) (2023)
- [Towards a Digital Twin Modeling Method for Container Terminal Port](https://mlsanity.com/paper/2503.13511/) (2025)

## Abstract (arXiv)

Background: The standardisation of Intermodal Loading Units (ILUs), including containers, semi-trailers, and swap bodies, has transformed global trade, yet efficient and robust identification remains an operational bottleneck in ports and terminals. Objective: To map Computer Vision (CV) methods for ILU identification, clarify terminology, summarise the evolution of proposed approaches, and highlight research gaps, future directions and their potential effects on terminal operations. Methods: Following PRISMA-ScR, we searched Google Scholar and dblp for English-language studies with quantitative results. After dual reviewer screening, the studies were charted across methods, datasets, and evaluation metrics. Results: 63 empirical studies on CV-based solutions for the ILU identification task, published between 1990 and 2025 were reviewed. Methodological evolution of ILU identification solutions, datasets, evaluation of the proposed methods and future research directions are summarised. A shift from static (e.g. OCR-gates) to vehicle mounted camera setups, which enables precise monitoring is observed. The reported results for end-to-end accuracy range from 5% to 96%. Conclusions: We propose standardised terminology, advocate for open-access datasets, codebases and model weights to enable fair evaluation and define future work directions. The shift from static to dynamic camera settings introduces new challenges that have transformative potential for transportation and logistics. However, the lack of public benchmark datasets, open-access code, and standardised terminology hinders the advancements in this field. As for the future work, we suggest addressing the new challenges emerged from vehicle mounted cameras, exploring synthetic data generation, refining the multi-stage methods into unified end-to-end models to reduce complexity, and focusing on contextless text recognition.