Defence and Systems Institute, UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, Australia.
College of Science and Engineering, Flinders University, Tonsley, SA 5042, Australia.
Sensors (Basel). 2021 Mar 5;21(5):1812. doi: 10.3390/s21051812.
Thermal infrared imaging provides an effective sensing modality for detecting small moving objects at long range. Typical challenges that limit the efficiency and robustness of the detection performance include sensor noise, minimal target contrast and cluttered backgrounds. These issues become more challenging when the targets are of small physical size and present minimal thermal signatures. In this paper, we experimentally show that a four-stage biologically inspired vision (BIV) model of the flying insect visual system have an excellent ability to overcome these challenges simultaneously. The early two stages of the model suppress spatio-temporal clutter and enhance spatial target contrast while compressing the signal in a computationally manageable bandwidth. The later two stages provide target motion enhancement and sub-pixel motion detection capabilities. To show the superiority of the BIV target detector over existing traditional detection methods, we perform extensive experiments and performance comparisons using high bit-depth, real-world infrared image sequences of small size and minimal thermal signature targets at long ranges. Our results show that the BIV target detector significantly outperformed 10 conventional spatial-only and spatiotemporal methods for infrared small target detection. The BIV target detector resulted in over 25 dB improvement in the median signal-to-clutter-ratio over the raw input and achieved 43% better detection rate than the best performing existing method.
热红外成像是一种有效的远距离小移动物体检测传感方式。限制检测性能效率和鲁棒性的典型挑战包括传感器噪声、最小目标对比度和杂乱背景。当目标物体尺寸较小且热信号很微弱时，这些问题会变得更加具有挑战性。在本文中，我们通过实验证明，受昆虫飞行视觉系统启发的四阶段视觉（BIV）模型具有出色的能力，可以同时克服这些挑战。该模型的前两个阶段抑制了时-空杂波，并增强了空间目标对比度，同时在可计算的带宽内压缩信号。后两个阶段提供了目标运动增强和亚像素运动检测能力。为了展示 BIV 目标检测器相对于现有传统检测方法的优越性，我们使用高比特深度、真实世界的小尺寸和微弱热信号目标的长距离红外图像序列进行了广泛的实验和性能比较。结果表明，BIV 目标检测器在红外小目标检测方面明显优于 10 种传统的仅空间和时空方法。与原始输入相比，BIV 目标检测器的中值信杂比提高了 25dB 以上，检测率比表现最好的现有方法提高了 43%。
