## 3 Data Preparation and Pre-Processing Methodology  
A quadcopter UAV based mission for the search and rescue of a target in a collapsed building was taken into consideration during augmentation of the input image data. We used a stereo camera to collect the 3D image information in SVO format. Sample images are presented in Figure 1. The image information is then converted to 2D stereo images for training the supervised deep neural network. Before the data were analyzed, they were organized into an appropriate form. The raw image data were prepared in such a way that they retain complete information about the environment they represent: the aspect of the environment that the data are describing. Random sample image inputs of the actual experimental indoor environment are shown in Figure 2. The integrity, completeness, validity, consistency and uniqueness of the dataset are maintained as much as possible. The whole preparation process contains tasks such as refining, integrating and transforming input data. For our small dataset, we applied an image augmentation technique to train the network. This augmentation technique enhances the size of input data by zooming, shearing, rotating, blurring, flipping and changing the color of already existing scarce input data. The technique performs transformations to yield believable-looking images in the scene. The network model makes use of this technique to perceive wider aspects of the input  
Figure 1: Experimental setup and sample stereo images of objects used to train the network. Figure 2: Sample random stereo images used to train our deep neural network. data. Deliberately introducing imperfections into our dataset was essential to making our model more resilient to the harsh realities it will encounter in real world situations. Degrading image quality by applying Gaussian blurring was one we applied to our images for this purpose. Figure 3 shows a random sample of the blurred images used for training. Many types of imperfections can make their way into an image: blur, poor contrast, noise, joint photographic experts group (JPEG) compression, and more. Of these, blurring is among the most detrimental to image classification. Blurring an image is taking neighboring pixels and averaging them, in effect reducing detail and creating what can be perceived as blur. When we implement different amounts of blur, we are determining how many neighboring pixels to include. We measured this spread from a single pixel as the standard deviation in both the horizontal and vertical directions. The larger the standard deviation, the more blur an image receives. In this work, we filtered the image with a Gaussian filter of 4 standard deviations. Image flipping is one of the commonly used approaches in image augmentation techniques. Flipping images vertically or horizontally does not alter the classes of objects in the images. Sample horizontally flipped (left and right) images used in the model training are displayed in Figure 4a. Figure 4: _Cont._  
Figure 3: Original (**top**) and blurred (**bottom**) sample images. Even though it is not as common as horizontal flipping, we flipped input images vertically (up-side-down) and the sample is displaced in Figure 3(b). Both of these horizontal and vertical flipping techniques align with the scenario of objects under a collapsed building. To make the network model invariant to the change in positions of objects in images, input images were randomly cropped. Each input image was randomly cropped from 10% to 100% of its original area, and the ratio of width to height of the region was randomly selected between 0.5 and 2. Sample cropped images are shown in Figure 4(a) with the width and height scaled to 180 pixels. Varying the colors of input images is another image augmentation technique. We varied the brightness, contrast, saturation and hue of input images. Brightness is randomly varied between 50% to 150% of the original image and sample images are as shown in Figure 4(b). Similarly, the hues of the input images were randomly varied.