In recent years, the number of crimes with weapons has grown on a large scale worldwide, mainly in locations where enforcement is lacking or possessing weapons is legal. It is necessary to combat this type of criminal activity to identify criminal behavior early and allow police and law enforcement agencies immediate action. Despite the human visual structure being highly evolved and able to process images quickly and accurately if an individual watches something very similar for a long time, there is a possibility of slowness and lack of attention. In addition, large surveillance systems with numerous equipment require a surveillance team, which increases the cost of operation. There are several solutions for automatic weapon detection based on computer vision; however, these have limited performance in challenging contexts. A systematic review of the current literature on deep learning-based weapon detection was conducted to identify the methods used, the main characteristics of the existing datasets, and the main problems in the area of automatic weapon detection. The most used models were the Faster R-CNN and the YOLO architecture. The use of realistic images and synthetic data showed improved performance. Several challenges were identified in weapon detection, such as poor lighting conditions and the difficulty of small weapon detection, the last being the most prominent. Finally, some future directions are outlined with a special focus on small weapon detection.

中文翻译：

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通过深度学习对监控录像中的武器检测进行系统审查

近年来，世界范围内使用武器的犯罪数量大规模增长，主要发生在执法不力或持有武器合法的地区。有必要打击此类犯罪活动，以便及早识别犯罪行为，并让警察和执法机构立即采取行动。尽管人类视觉结构高度进化，如果一个人长时间观看非常相似的东西，能够快速准确地处理图像，但有可能会变得缓慢和缺乏注意力。此外，拥有众多设备的大型监控系统需要监控团队，这增加了运营成本。基于计算机视觉的自动武器检测有多种解决方案；然而，这些在具有挑战性的环境中的性能有限。对当前基于深度学习的武器检测文献进行了系统回顾，以确定自动武器检测领域所使用的方法、现有数据集的主要特征以及主要问题。最常用的模型是 Faster R-CNN 和 YOLO 架构。真实图像和合成数据的使用显示出性能的提高。武器检测面临一些挑战，例如照明条件差和小型武器检测困难，最后一个是最突出的。最后，概述了一些未来的方向，特别关注小型武器探测。