Object detectors have improved in recent years, obtaining better results and faster inference time. However, small object detection is still a problem that has not yet a definitive solution. The autonomous weapons detection on Closed-circuit television (CCTV) has been studied recently, being extremely useful in the field of security, counter-terrorism, and risk mitigation. This article presents a new dataset obtained from a real CCTV installed in a university and the generation of synthetic images, to which Faster R-CNN was applied using Feature Pyramid Network with ResNet-50 resulting in a weapon detection model able to be used in quasi real-time CCTV (90 ms of inference time with an NVIDIA GeForce GTX-1080Ti card) improving the state of the art on weapon detection in a two stages training. In this work, an exhaustive experimental study of the detector with these datasets was performed, showing the impact of synthetic datasets on the training of weapons detection systems, as well as the main limitations that these systems present nowadays. The generated synthetic dataset and the real CCTV dataset are available to the whole research community.

近年来，目标检测器得到了改进，获得了更好的结果和更快的推理时间。但是，小物体检测仍然是尚未解决的问题。闭路电视（CCTV）上的自动武器检测技术最近得到了研究，在安全，反恐和减轻风险领域中非常有用。本文介绍了从安装在大学中的真实CCTV上获得的新数据集以及合成图像的生成，使用具有ResNet-50的功能金字塔网络将Faster R-CNN应用到了该数据集中，从而可以在准条件下使用武器检测模型实时闭路电视（使用NVIDIA GeForce GTX-1080Ti卡的推理时间为90毫秒）通过两个阶段的训练提高了武器检测的最新水平。在这项工作中 对带有这些数据集的探测器进行了详尽的实验研究，显示了合成数据集对武器探测系统训练的影响以及这些系统现今的主要局限性。生成的合成数据集和真实的CCTV数据集可供整个研究社区使用。