论文标题
通过卷积神经网络检测枪支检测：将语义分割模型与端到端解决方案进行比较
Firearm Detection via Convolutional Neural Networks: Comparing a Semantic Segmentation Model Against End-to-End Solutions
论文作者
论文摘要
威胁发现武器和实时视频的侵略行为可用于快速检测和预防潜在的致命事件，例如恐怖主义，一般刑事犯罪甚至家庭暴力。实现这一目标的一种方法是使用人工智能，尤其是用于图像分析的机器学习。在本文中，我们将传统的单片端到端深度学习模型与以前提出的模型进行比较，该模型基于通过语义分割来检测消防器的简单神经网络的集合。我们从不同的角度评估了这两个模型，包括准确性，计算和数据复杂性，灵活性和可靠性。我们的结果表明，与经典的深层模型相比，语义分割模型在低数据环境中提供了相当大的灵活性和弹性，尽管其配置和调整在达到与端到端模型相同的准确性方面构成了挑战。
Threat detection of weapons and aggressive behavior from live video can be used for rapid detection and prevention of potentially deadly incidents such as terrorism, general criminal offences, or even domestic violence. One way for achieving this is through the use of artificial intelligence and, in particular, machine learning for image analysis. In this paper we conduct a comparison between a traditional monolithic end-to-end deep learning model and a previously proposed model based on an ensemble of simpler neural networks detecting fire-weapons via semantic segmentation. We evaluated both models from different points of view, including accuracy, computational and data complexity, flexibility and reliability. Our results show that a semantic segmentation model provides considerable amount of flexibility and resilience in the low data environment compared to classical deep model models, although its configuration and tuning presents a challenge in achieving the same levels of accuracy as an end-to-end model.
