一种FPGA实现的复杂背景红外小目标检测网络

An infrared small target detection network under various complex backgrounds realized on FPGA

红外(IR)小目标检测算法具有检测率高、虚警率低、实时性好等优点在红外遥感领域有重要的应用价值。由于复杂背景下小目标对比度低和信噪比(SNR)低,传统红外小目标检测算法难以保证检测性能。在强鲁棒性的红外小目标检测网络(RISTDnet)基础上,面向更为多样的目标结构特征和更高的实时处理性能要求,提出一种增强型红外小目标检测网络(EISTDnet)与其基于现场可编程逻辑门阵列(FPGA)高性能并行处理的算法。EISTDnet构造了手工特征算法与卷积神经网络相结合的多尺度小目标特征提取框架,采用多级展开思路对卷积核尺寸进行归一化设计,并通过数据深度复用和多维循环并行展开有效提高推理阶段实时处理性能。实验结果表明:采用单片FPGA实现的EISTDnet能够快速实时检测复杂背景下不同大小、低信噪比的小目标,与现有5种算法相比在10^(-3)低虚警率下平均检测率提升49.5%,与RISTDnet相比,在实时处理速度提高1.33倍的优势下,对低信噪比条状小目标检测率提升29.4%,所提算法具有更好的有效性和鲁棒性。

The infrared(IR)small target detection algorithm with high detection rate,low false alarm rate and good real-time performance has important application value in the field of IR remote sensing.Traditional IR small targets detection algorithms cannot guarantee the detection performance due to the low contrast and low signal-to-noise ratio(SNR)of small targets under various complex backgrounds.Based on robust infrared small target detection network(RISTDnet)proposed,for more diverse target structure characteristics and higher real-time processing performance requirements,an enhanced infrared small target detection network(EISTDnet)and its field programmable logic gate array(FPGA)based high-performance parallel processing method are proposed.In EISTDnet,a multi-scale small target feature extraction framework that combines manual feature methods and convolutional neural networks is constructed,the size of the convolution kernel is normalized by the idea of multi-level expansion,and real-time processing performance in the inference stage is effectively improved through deep data reuse and multi-dimensional loop parallel unfolding.Experimental results show that the EISTDnet realized on a single FPGA can quickly detect small targets with different sizes and low SNR in various complex backgrounds in real time.Compared with the existing 5 algorithms,the average detection rate is increased by 49.5%with a low false alarm rate of 10^(-3).Compared with RISTDnet,the real-time processing speed is increased by 1.33 times,and the detection rate of low SNR strip small targets is increased by 29.4%.EISTDnet has better effectiveness and robustness.