对比YOLOv12、YOLOv11与RF-DETR检测集装箱损伤效果。
Container damage detection using advanced computer vision model Yolov12 vs Yolov11 vs RF-DETR A comparative analysis
- 用三类先进视觉模型在278张标注图像上训练比对。
- YOLOv11和v12的mAP@50达81.9%，高于RF-DETR的77.7%。
- RF-DETR在罕见损伤检测中表现更优，识别更准且置信度高。
集装箱是物流行业核心载体，使用寿命超20年。长期使用中，受机械与自然因素影响易出现损伤，不仅威胁作业人员安全，也带来企业责任风险。及时检测损伤对延长寿命与规避隐患至关重要。本文对比三种前沿计算机视觉模型——YOLOv12、YOLOv11与RF-DETR在集装箱损伤检测中的性能。基于包含278张标注图像的数据集进行训练、验证与测试，评估指标为mAP与精度。结果显示，YOLOv11与YOLOv12的mAP@50达到81.9%，高于RF-DETR的77.7%；但在检测非常见损伤类型时，RF-DETR整体表现更优，能更准确识别损伤并保持高置信度。
原文摘要 · Abstract (English)
Containers are an integral part of the logistics industry and act as a barrier for cargo. A typical service life for a container is more than 20 years. However, overtime containers suffer various types of damage due to the mechanical as well as natural factors. A damaged container is a safety hazard for the employees handling it and a liability for the logistic company. Therefore, a timely inspection and detection of the damaged container is a key for prolonging service life as well as avoiding safety hazards. In this paper, we will compare the performance of the damage detection by three state-of-the-art advanced computer vision models Yolov12, Yolov11 and RF-DETR. We will use a dataset of 278 annotated images to train, validate and test the model. We will compare the mAP and precision of the model. The objective of this paper is to identify the model that is best suited for container damage detection. The result is mixed. mAP@50 score of Yolov11 and 12 was 81.9% compared to RF-DETR, which was 77.7%. However, while testing the model for not-so-common damaged containers, the RF-DETR model outperformed the others overall, exhibiting superiority to accurately detecting both damaged containers as well as damage occurrences with high confidence.
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