Military object detection and identification is a key capability in surveillance and reconnaissance. It is a major factor in warfare effectiveness and warfighter survivability. Inexpensive, portable, and rapidly deployable small unmanned aerial systems (sUAS) in conjunction with powerful deep learning (DL) based object detection models are expected to play an important role for this application. To prove overall feasibility of this approach, this paper discusses some aspects of designing and testing of an automated detection system to locate and identify small firearms left at the training range or at the battlefield. Such a system is envisioned to involve an sUAS equipped with a modern electro-optical (EO) sensor and relying on a trained convolutional neural network (CNN). Previous study by the authors devoted to finding projectiles on the ground revealed certain challenges such as small object size, changes in aspect ratio and image scale, motion blur, occlusion, and camoufiage. This study attempts to deal with these challenges in a realistic operational scenario and go further by not only detecting different types of firearms but also classifying them into different categories. This study used a YOLOv2 CNN (ResNet-50 backbone network) to train the model with ground truth data and demonstrated a high mean average precision (mAP) of 0.97 to detect and identify not only small pistols but also partially occluded rifies.(c) 2023 China Ordnance Society. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).
