Deep convolutional neural networks have achieved remarkable results for detecting large and medium sized objects in images. However, the ability to detect smallobjects has yet to achieve the same level performance. Our focus is on applications that require the accurate detection and localization of small moving objects that are distantfrom the sensor. We first examine the ability of several state-of-the-art object detection networks (YOLOv3 and Mask R-CNN) to find small moving targets in infraredimagery using a publicly released dataset by the US Army Night Vision and Electronic Sensors Directorate. We then introduce a novel Moving Target Indicator Network (MTINet) and repurpose a hyperspectral imagery anomaly detection algorithm, the Reed-Xiaoli detector, for detecting small moving targets. We analyze the robustness and applicability of these methods by introducing simulated sensor movement to the data. Compared with other state-of-the-art methods, MTINet and the ARX algorithm achieve ahigher probability of detection at lower false alarm rates. Specifically, the combination of the algorithms results in a probability of detection of approximately 70% at a low false alarm rate of 0.1, which is about 50% greater than that of YOLOv3 and 65% greater than Mask R-CNN
