Detecting knives in surveillance videos are very urgent for public safety. In general, the research in identifying dangerous weapons is relatively new. Knife detection is a very challenging task because knives vary in size and shape. Besides, it easily reflects lights that reduce the visibility of knives in a video sequence. The reflection of light on the surface of the knife and the brightness on its surface makes the detection process extremely difficult, even impossible. This paper presents an adaptive technique for brightness enhancement of knife detection in surveillance systems. This technique overcomes the brightness problem that faces the steel weapons and improves the knife detection process. It suggests an automatic threshold to assess the level of frame brightness. Depending on this threshold, the proposed technique determines if the frame needs to enhance its brightness or not. Experimental results verify the efficiency of the proposed technique in detecting knives using the deep transfer learning approach. Moreover, the most four famous models of deep convolutional neural networks are tested to select the best in detecting knives. Finally, a comparison is made with the-state-of-the-art techniques, and the proposed technique proved its superiority.

为了公共安全，在监控录像中检测刀具非常紧急。通常，识别危险武器的研究相对较新。由于刀具的尺寸和形状各不相同，因此检测刀具是一项非常具有挑战性的任务。此外，它很容易反射光线，从而降低了视频序列中刀具的可见性。刀表面的光反射和刀表面的亮度使检测过程极为困难，甚至是不可能的。本文提出了一种自适应技术，用于在监视系统中增强刀具检测的亮度。该技术克服了钢制武器面临的亮度问题，并改善了刀具检测过程。它建议使用自动阈值来评估帧亮度水平。根据此阈值，所提出的技术确定框架是否需要增强其亮度。实验结果证明了该技术在使用深度转移学习方法检测刀具方面的效率。此外，对深度卷积神经网络的最著名的四个模型进行了测试，以选择最适合检测刀具的刀具。最后，与最先进的技术进行了比较，提出的技术证明了其优越性。