用YOLOv8实时检测枪支、刀具和钝器，提升公共安全监控效率。
Real-Time Threat Detection from Surveillance Cameras using Machine Learning
- 基于自建移动拍摄数据集与公开数据融合训练YOLOv8模型。
- 延长训练时间使钝器类召回率与平均精度显著提升。
- 适合校园、交通枢纽等场景部署，兼顾准确率与实时性。
在人口密集的城市环境中保障公共安全仍是重大挑战，亟需智能自动化视频监控系统。传统监控依赖人工值守，效率低且易受疲劳、延迟响应和误判影响。为此，本文提出一种基于目标检测的实时监控框架，聚焦于印度场景中常见的枪支、刀具及铁棍、木棍、塑料棍等钝器的检测。关键贡献在于构建了一个由移动相机采集的自定义数据集，包含336张钝器标注图像，并与公开的7,623张枪支刀具图像合并，形成共7,959张图像的三分类数据集（枪支、刀具、钝器）。使用该数据集训练基于YOLOv8的目标检测模型以实现实时性能。实验表明，增加训练时长可显著提升钝器类别的召回率与平均精度，且无过拟合迹象。整体框架在准确率与效率间取得良好平衡，适用于校园、公共场所及交通区域等实际监控环境。
原文摘要 · Abstract (English)
Ensuring public safety in densely populated urban environments remains a critical challenge, necessitating the deployment of intelligent and automated video surveillance systems. Traditional surveillance approaches rely heavily on manual monitoring, which is inefficient and susceptible to human fatigue, delayed response, and observational errors. To overcome these limitations, this work presents a real-time object detection-based surveillance framework. The proposed system focuses on detecting guns, knives, and region-specific blunt objects commonly involved in violent activities in Indian surveillance scenarios. A key contribution of this work is the use of a custom-created dataset collected using a mobile camera, consisting of 336 labeled images of blunt objects such as iron rods, wooden sticks, and plastic rods. This dataset is combined with a publicly available dataset of 7,623 images of guns and knives, forming a consolidated dataset of 7,959 images across three classes: gun, knife, and blunt object. The combined dataset is used to train a YOLOv8-based object detection model for real-time performance. Experimental evaluation shows that increasing the training duration significantly improves recall and average precision for the blunt object class without signs of overfitting. Overall, the proposed framework achieves an effective balance between accuracy and efficiency, making it suitable for deployment in real-world surveillance environments such as campuses, public spaces, and transportation areas.
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