动态大视角场景融合帧间信息与模板匹配的低慢小无人机目标检测

Low slow small UAV targets detection by fused using inter-frame information and emplate matching in dynamic large-view scene

为了提升动态大视角场景下对极小像素的低慢小无人机目标探测能力,本文提出了融合帧间信息与模板匹配的检测方法。首先,设计了一种动态信息提取模块,通过滤除背景信息干扰,引导算法关注动态变化的小目标区域;其次,提出多模板匹配策略对筛选出的动态区域进行相似度判断,完成无人机目标检测;最后,在天空、山地、楼宇等不同背景下,按照不同尺寸、不同模态进行了无人机目标检测实验。结果表明,本文方法可有效弥补深度学习方法对大视角极小像素目标检测的不足,对低慢小目标检测准确率达到0.81,虚警率为0.06,在像素占比不小于0.01%数据集上准确率能达到0.70。该方法适应可见光、红外多种模态数据处理,可满足后续多种智能算法组合探测应用。

In order to improve the detection ability of low,slow and small unmanned aerial vehicle(UAV)targets with very small pixels in dynamic,wide-angle scenes,this paper proposes a detection method that integrates inter frame information with template matching.Firstly,a dynamic information extraction module was designed to guide the algorithm to focus on dynamically changing small target areas by filtering out background information interference.Secondly,a multi template matching strategy is adopted to determine the similarity of the selected dynamic regions and complete drone target detection.Finally,drone target detection experiments were conducted under different backgrounds such as sky,mountains,and buildings,with different sizes and modes.The results show that the method proposed in this paper can effectively compensate for the shortcomings of deep learning methods in detecting extremely small pixel targets in wideangle views.The detection accuracy of low,slow and small targets reaches 0.81,with a false alarm rate of 0.06,and the accuracy can reach 0.70 on datasets with pixel ratios not less than 0.01%.The method is suitable for data processing in different modes such as visible light and infrared,and can meet the application needs of various intelligent algorithm combinations for detection in the future.