一种地对空红外弱小目标自主发现设备
A Ground-to-Air Infrared Small and Small Target Autonomous Detection Equipment

在军事作战领域中，准确的目标识别是确保诸如预警系统、拦截导弹、侦察装备及远程打击武器等各类军事资产能够充分发挥其战术与战略效能的核心要素。然而，在复杂背景的干扰下，通过雷达侦测、光电侦测、电磁频谱侦测等常规侦测手段已经难以满足现代战场环境下对于导弹、无人机等小目标的预警监测需求。本文针对当前战场环境中无人机等弱小空中目标监测与识别的紧迫需求，设计了一种地对空红外弱小目标自主发现设备。设备由电源模块、图像采集模块、嵌入式算法处理模块、显示模块、嵌入式算法处理模块组成，成本低廉、小巧轻便、便携性强。同时，设备引入最新的DNA-Net模型来进行红外小目标识别。鉴于传统侦测手段如雷达、光电及电磁侦测在便携性、隐蔽性上的局限，以及难以满足复杂环境中的实时监测挑战，本研究聚焦于利用红外成像技术与深度学习算法的结合，以提高弱小目标的发现能力。
In the field of military operations, accurate target identification is a core element to ensure that military assets such as early warning systems, interceptor missiles, reconnaissance equipment and long-range strike weapons can achieve their full tactical and strategic effectiveness. However, under the interference of complex background, conventional detection methods such as radar detection, photoelectric detection and electromagnetic spectrum detection have been difficult to meet the needs of early warning and monitoring of small targets such as missiles and UAVs in modern battlefield environment. Aiming at the urgent need of monitoring and recognition of small and small targets such as unmanned aerial vehicles (UAVs) in the current battlefield environment, this paper designs a surface-to-air infrared small and small targets autonomous detection equipment. The device is composed of power module, image acquisition module, embedded algorithm processing module, display module and embedded algorithm processing module, with low cost, compact and portable. At the same time, the device introduces the latest DNA-Net model for infrared small target recognition. In view of the limitations of traditional detection methods such as radar, photoelectric and electromagnetic detection in portability and concealability, as well as the difficulty in meeting the challenges of real-time monitoring in complex environments, this research focuses on the combination of infrared imaging technology and deep learning algorithm to improve the detection ability of dim targets.