用计算机视觉自动识别集装箱等运输单元，提升港口效率

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.