## 6 Object Detection Modeling  
Training data were generated with MAVS. Scenes were automatically generated with random degrees of surface roughness. The buildings and trees in each scene were randomly oriented and scaled to produce a wide range of different training data. In addition, some vehicles were placed in the scenes in random positions and orientations. For example, some red vehicles can be seen in bottom right corner Figure 10a.  
Figure 9: Example box-labeling of real-world data.  
To match the weather conditions of the real data, the MAVS simulations used mid-day conditions with no cloud cover, rain, or fog. The camera altitude was 122 m, like the real camera. The simulated camera had a horizontal field-of-view of 60.5\({}^{\circ}\). Simulations were run on Mississippi State University's supercomputer, Shadow, a Cray CS300-LC on multiple Xeon-Phi cores.  
Object detection in aerial images is a challenging task as some objects are only a few pixels wide. We have implemented keras-retinanet on the simulated aerial data generated using MAVS. RetinaNet is one of the best single stage object detection models that has proven to work well with dense and small-scale objects [38]. Moreover, it is one of the popular object detection models to be used with aerial imagery. RetinaNet makes use of keras, tensorflow, tensorboard, CUDA, CUDNN, and several other Python libraries.  
Keras-RetinaNet uses feature pyramid network (FPN) and focal loss for training. FPN is a structure for multiscale object detection which combines low resolution, high resolution, and semantically weak features in a top down pyramid architecture [37]. The focal loss is designed to address the problems with imbalance where there are many background classes and a few foreground classes.  
The simulated aerial dataset consists of 871 aerial images of 640 \(\times\) 480 resolution as shown in Figure 10a. Keras-RetinaNet framework with ResNet 50 as backbone was implemented for object detection. The training dataset has 696 images with bounding box annotations and labels in PASCAL VOC format. The model was evaluated with 175 images in the validation dataset. Anchor boxes are fixed size boxes that the model uses to predict the bounding box of an object. The hyperparameters used in the model are the pixel size of the anchor box, the distance between the centers of two neighboring anchor boxes (strides), and the height/width ratio of the box to predict a relative scale of the object.  
In this preliminary work, we detected \"buildings\" in the aerial imagery. Each location on a given feature map has nine anchor boxes (at three scales and three ratios). The model is trained using 15 epochs and 500 steps. The metrics used to evaluate the model are mAP (mean Average Precision) and IoU (Intersection of Union). The mean average precision value is the average AP over all classes and the IoU indicates how much bounding boxes overlap, which is crucial in determining the true positives and false positives. Our results achieved a mAP accuracy of 88% at IoU = 0.3. As shown in Figure 10b, the algorithm detected each roof section as a building. The detected image has one false positive, where a truck on the top right was detected as a \"building\". As seen in Figure 10a, the building roofs are in blackish gray except a few roofs with red color. There were very few images with red roof structures and the algorithm detected a part of these structures too. Overall, the deep learning network successfully detected objects in the aerial imagery.