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Abstract：
The minimum resolvable temperature difference (MRTD) at which a four-rod target can be resolved is a critical parameter used to assess the comprehensive performance of thermal imaging systems, which is important for technological innovation in military and other fields. Recently, there have been some attempts to use an automatic objective approach based on deep learning to take the place of the classical manual subjective MRTD measurement approach, which is strongly affected by the psychological subjective factors of the experimenter and is limited in accuracy and speed. However, the scale variability of four-rod targets and the low pixels of infrared thermal cameras have turned out to be a challenging problem for automatic MRTD measurement. We propose a multiscale deblurred feature extraction network (MDF-Net), a backbone based on a yolov5 neural network, in an attempt to solve the aforementioned problem. We first present a global attention mechanism (GAM) attention module to represent strong images of the four-rod targets. Next, a Rep VGG module is introduced to decrease the blur. Our experiments show that the proposed method achieves the desired effect and state-of-the-art detection results, which innovatively improve the accuracy of four-rod target detection to 82.3% and thus make it possible for the thermal imagers to see further and to respond faster and more accurately.
摘要：
可以分辨四杆目标的最小可分辨温差（MRTD）是用于评估热成像系统综合性能的关键参数，这对军事和其他领域的技术创新至关重要。最近,有一些尝试使用基于深度学习的自动客观方法来代替经典的手动主观MRTD测量方法，受到实验者心理主观因素的强烈影响，准确性和速度有限。然而,四杆目标的尺度变异性和红外热像仪的低像素已成为自动MRTD测量的难题。我们提出了一种多尺度去模糊特征提取网络(MDF-Net)，基于yolov5神经网络的骨干，试图解决上述问题。我们首先提出了一个全球注意力机制（GAM）注意力模块，以表示四杆目标的强大图像。接下来,引入了RepVGG模块以减少模糊。实验表明,该方法达到了预期的效果和检测效果,创新地将四杆目标检测的精度提高到82.3%，从而使热成像仪能够看得更远，响应更快、更准确。
