# A Military Object Detection Model of UAV Reconnaissance Image and Feature Visualization
## 1 Introduction  
Unmanned Aerial Vehicle (UAV) reconnaissance has been an important means of obtaining information in warfare, in which military object detection is one of the most important tasks since it can identify and locate objects. However, there are still some challenges in military object detection of UAV reconnaissance images. First of all, military objects from UAV image data are difficult to obtain due to confidentiality. Secondly, UAVs are susceptible to bad weather during reconnaissance, resulting in distortions, such as fog blur and motion blur, which will reduce the image contrast and image quality. It is more difficult to detect an object in the images with low contrast and low quality. Thirdly, the resolution of UAV images is usually high due to the wide reconnaissance field of UAVs, and the objects are relatively small. Moreover, the background is also extremely complex due to military camouflage, which also brings lots of difficulties. Therefore, military object detection of UAV reconnaissance images is still an open problem. To deal with the lack of tank image databases of UAV reconnaissance, we choose three typical main battle tanks and customize the miniaturized ratio model of the tanks. Then, we build a simulation platform of UAV low-altitude reconnaissance to capture image data of the tanks in different scenes of a wild battlefield with an RGB camera. Finally, we label the tanks in the UAV image in a manual manner and construct the UAV image tank dataset (UAVT-3). The traditional object detection algorithm usually extracts the features manually, which has a low detection accuracy. Inspired by the great success of deep learning appliedin a variety of vision tasks [1], more and more Convolutional Neural Network (CNN)-based detection algorithms have recently improved the detection accuracy. The existing CNN detection algorithms can be roughly divided into two-stage algorithms [2; 3; 4] and one-stage [5; 6; 7; 8; 9] algorithms. In the two-stage algorithms, a series of candidate boxes were generated at the first stage, which was then classified by a CNN at the second stage. In the one-stage algorithms, the task of target box location was modeled as a regression problem directly, which only looked at the image one time with a single network. The series of Region-based Convolutional Networks (R-CNNs) are typical two-stage detection models. R-CNN [2] is the first detection model exploring CNN to extract visual features, which also adopted supervised pre-training of large samples and domain-specific fine-tuning to avoid the over-fitting problem in training. Although R-CNN yielded a performance boost, there are lots of repeated computations in feature extraction for each candidate region. Spatial pyramid pooling networks (SPnets) [10] were proposed to speed up R-CNN by sharing computation, which computed a convolutional feature map for the entire input image and then classified each object proposal using a feature vector extracted from the shared feature map. Fast R-CNN [3] proposed a new training algorithm to fix the disadvantages of R-CNN and SPPnets, which improved the speed and accuracy. Faster R-CNN [4] introduced a Region Proposal Network (RPN) for nearly cost-free region proposals by sharing the convolutional features with the detection network. The You Only Look Once algorithms (YOLOs) and Single Shot multi-box Detector (SSD) are the most popular one-stage models. The first one-stage model, YOLOv1 [5], framed object detection as a regression problem to spatially separate bounding boxes and associate class probabilities. It proposed a single neural network to predict bounding boxes and class probabilities directly from the entire image in one evaluation, which can be optimized end-to-end directly. SSD [6] proposed a deep CNN-based object detector without resampling pixels or features for bounding box hypotheses, which took feature maps of different scales for detection, and directly used convolution instead of a full connection network to obtain detection results.