Vrsalovic Ivan, Lerga Jonatan, Ivasic-Kos Marina
Faculty of Informatics and Digital Technologies, University of Rijeka, 51000 Rijeka, Croatia.
Faculty of Engineering, University of Rijeka, 51000 Rijeka, Croatia.
Sensors (Basel). 2025 May 2;25(9):2872. doi: 10.3390/s25092872.
In this paper, we propose a system for the real-time automatic detection of abandoned luggage in an airport recorded by surveillance cameras. To do this, we use an adapted YOLOv11-s model and a proposed algorithm for detecting unattended luggage. The system uses the OpenCV library for the video processing of the recorded footage, a detector, and an algorithm that analyzes the movement of a person and their luggage and evaluates their spatial and temporal relationships to determine whether the luggage is truly abandoned. We used several popular deep convolutional neural network architectures for object detection, e.g., Yolov8, Yolov11, and DETR encoder-decoder transformer with a ResNet-50 deep convolutional backbone, we fine-tuned them on our dataset, and compared their performance in detecting people and luggage in surveillance scenes recorded by an airport surveillance camera. The fine-tuned model significantly improved the detection of people and luggage captured by the airport surveillance camera in our custom dataset. The fine-tuned YOLOv8 and YOLOv11 models achieved excellent real-time results on a challenging dataset consisting only of small and medium-sized objects. They achieved real-time precision (mAP) of over 88%, while their precision for medium-sized objects was over 96%. However, the YOLOv11-s model achieved the highest precision in detecting small objects, corresponding to 85.8%, which is why we selected it as a component of the abandoned luggage detection system. The abandoned luggage detection algorithm was tested in various scenarios where luggage may be left behind and in situations that may be potentially suspicious and showed promising results.
在本文中，我们提出了一种用于实时自动检测机场监控摄像头记录的无人看管行李的系统。为此，我们使用了经过改进的YOLOv11-s模型和一种用于检测无人看管行李的算法。该系统使用OpenCV库对录制的视频进行处理，利用一个检测器以及一种算法来分析人员及其行李的移动情况，并评估它们的空间和时间关系，以确定行李是否真的被遗弃。我们使用了几种流行的用于目标检测的深度卷积神经网络架构，例如Yolov8、Yolov11以及带有ResNet-50深度卷积主干的DETR编码器-解码器变换器，我们在我们的数据集上对它们进行了微调，并比较了它们在机场监控摄像头记录的监控场景中检测人员和行李的性能。经过微调的模型显著提高了在我们的自定义数据集中由机场监控摄像头捕捉到的人员和行李的检测率。经过微调的YOLOv8和YOLOv11模型在一个仅由中小型物体组成的具有挑战性的数据集中取得了出色的实时结果。它们实现了超过88%的实时精度（平均精度均值），而它们对中型物体的精度超过了96%。然而，YOLOv11-s模型在检测小物体方面达到了最高精度，为85.8%，这就是为什么我们选择它作为无人看管行李检测系统的一个组件。无人看管行李检测算法在各种行李可能被遗留的场景以及可能存在潜在可疑情况的场景中进行了测试，并显示出了有前景的结果。
