Weapon and gun violence have recently become a pressing issue today. The degree of these crimes and activities has risen to the point of being termed as an epidemic. This prevalent misuse of weapons calls for an automatic system that detects weapons in real-time. Real-time surveillance video is captured and recorded in almost all public forums and places. These videos contain abundant raw data which can be extracted and processed into meaningful information. This paper proposes a novel pipeline consisting of an ensemble of convolutional neural networks with distinct architectures. Each neural network is trained with a unique mini-batch with little to no overlap in the training samples. This paper will present several promising results using multiple datasets associated with comparing the proposed architecture and state-of-the-art (SoA) models. The proposed pipeline produced an average increase of 5% in accuracy, specificity, and recall compared to the SoA systems.

翻译：武器与枪支暴力近日已成为一个紧迫的社会问题。此类犯罪活动的严重程度已升级至被称为"流行病"的程度。这种普遍的武器滥用现象催生了实时自动检测武器的系统需求。公共场所的实时监控视频被广泛采集和记录，这些视频包含大量可提取并转化为有意义信息的原始数据。本文提出了一种新颖的流水线架构，由多个具有不同结构的卷积神经网络集成而成。每个神经网络使用独特的微型批次进行训练，训练样本之间几乎无重叠。本文将展示基于多个数据集对比所提架构与当前最优模型的多项显著成果。相比当前最优系统，该流水线在准确率、特异性和召回率上平均提升了5%。