Nguyen Thi Phuong Thanh, Cho Gyu Sung, Chatterjee Indranath
Department of Port Logistics System, Tongmyong University, Busan, Republic of Korea.
Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, UK.
Sci Prog. 2025 Jan-Mar;108(1):368504251314084. doi: 10.1177/00368504251314084.
Ensuring the integrity of shipping containers is crucial for maintaining product quality, logistics efficiency, and safety in the global supply chain. Damaged containers can lead to significant economic losses, delays, and safety hazards. Traditionally, container inspections have been manual, which are labor-intensive, time-consuming, and error-prone, especially in busy port environments. This study introduces an automated solution using the YOLO-NAS model, a cutting-edge deep learning architecture known for its adaptability, computational efficiency, and high accuracy in object detection tasks. Our research is among the first to apply YOLO-NAS to container damage detection, addressing the complex conditions of seaports and optimizing for high-speed, high-accuracy performance essential for port logistics. Our method showcases YOLO-NAS's superior efficacy in detecting container damage, achieving a mean average precision (mAP) of 91.2%, a precision rate of 92.4%, and a recall of 84.1%. Comparative analyses indicate that YOLO-NAS consistently outperforms other leading models like YOLOv8 and Roboflow 3.0, which showed lower mAP, precision, and recall values under similar conditions. Additionally, while models such as Fmask-RCNN and MobileNetV2 exhibit high training accuracy, they lack the real-time assessment capabilities critical for port applications, making YOLO-NAS a more suitable choice. The successful integration of YOLO-NAS for automated container damage detection has significant implications for the logistics industry, enhancing port operations with reliable, real-time inspection solutions that can seamlessly integrate into predictive maintenance and monitoring systems. This approach reduces operational costs, improves safety, and lessens the reliance on manual inspections, contributing to the development of "smart ports" with higher efficiency and sustainability in container management.
确保运输集装箱的完整性对于维持全球供应链中的产品质量、物流效率和安全至关重要。受损的集装箱可能会导致重大经济损失、延误和安全隐患。传统上，集装箱检查是人工进行的，这既耗费人力、时间，又容易出错，尤其是在繁忙的港口环境中。本研究引入了一种使用YOLO-NAS模型的自动化解决方案，YOLO-NAS是一种前沿的深度学习架构，以其在目标检测任务中的适应性、计算效率和高精度而闻名。我们的研究是首批将YOLO-NAS应用于集装箱损坏检测的研究之一，解决了海港的复杂情况，并针对港口物流所需的高速、高精度性能进行了优化。我们的方法展示了YOLO-NAS在检测集装箱损坏方面的卓越功效，平均精度均值（mAP）达到91.2%，精确率为92.4%，召回率为84.1%。对比分析表明，YOLO-NAS始终优于其他领先模型，如YOLOv8和Roboflow 3.0，在类似条件下，这些模型的mAP、精确率和召回率值较低。此外，虽然Fmask-RCNN和MobileNetV2等模型在训练时表现出较高的准确率，但它们缺乏港口应用所需的实时评估能力，这使得YOLO-NAS成为更合适的选择。成功集成YOLO-NAS进行自动化集装箱损坏检测对物流行业具有重大意义，通过可靠的实时检查解决方案增强港口运营，这些解决方案可以无缝集成到预测性维护和监测系统中。这种方法降低了运营成本，提高了安全性，并减少了对人工检查的依赖，有助于集装箱管理中更高效率和可持续性的“智能港口”的发展。
