## 1 Introduction  
The emergence of artificial intelligence and computer vision technologies bring forth a wide range of applications. As a result, various unmanned systems are being deployed in both civilian and military domains. Equipped with these technologies, self-driving cars [1; 2; 3; 4] and autonomously navigating UAVs [5; 6; 7; 8; 9] are being integrated into our daily life. All of these and other important applications of integrated artificial intelligence and computer vision technologies rely, in one way or another, on training neural networks, which is crucial for the classification of objects in images taken by visual sensors. Moreover, the ability of a computing machine to autonomously detect and classify objects leveraged the autonomous navigation of unmanned aerial vehicles in cluttered environments. This capability further incites a wide range of applications of UAVs. UAV missions, such as door-to-door package delivery, search and rescue of victims in a collapsed building, indoor first aid, and target tracking in urban environments, demand that the UAV has environmental perception. To this end, training a companion computer onboard the UAV is mandatory. Training computing machines to perceive the surrounding environment is a state-of-the-art technology, generally known by the name \"machine learning\": a sub-discipline of artificial intelligence. For the process of machine learning, a network of mathematical abstracts (neurons) are layered in structured way. The depth of the network is determined by its number of layers and training this deep network to hierarchically extract desired features in input data is referred to as \"deep learning\". Various definitions of deep learning were reviewed by [PERSON] et al. [10]. A comprehensive review of deep learning and its variants was presented by [PERSON] et al. [11] and [PERSON] [12]. A survey of the wide range of applications of deep learning as well as its challenges and future directions was reported by [PERSON] et al. [13]  
There are various deep learning approaches where supervised deep learning is one of the well known and widely used approaches. In this type of neural network learning approach, the dataset is labeled manually for training the network. The three commonly known neural networks to which supervised deep learning is applied are the convolutional neural network (CNN), artificial neural network (ANN), and recurrent neural network (RNN). Ever since its conception, supervised deep learning is being implemented in various areas such as the autonomous navigation of both ground [14] and aerial vehicles [15], speech and pattern recognition [16], and medical image analysis [17]. The commonly utilized network that is often implemented in deep learning for object detection and classification is CNN [18; 19; 20; 21; 22]. This CNN is proved to outperform other networks on various tasks [23]. There are many variants of CNN and their differences are dictated by the network parameters, such as number of layers, number of intra-layer neurons and the types of inter-layer connections in the architecture. These parameters greatly affect the efficiency of the performance of a given CNN. An extensive survey of the recent architectures of deep CNN was reported by [PERSON] et al. [24]. [PERSON] et al. [25] analyzed three variants of CNN--AlexNet, GoogleNet and ResNet50--and reported that the number of layers in a network architecture affects the performance of CNN. [PERSON] et al. [26] found that the increase in the number of layers of CNN enhances the performance accuracy of the network. [PERSON] et al. [27] also proposed that increasing the depth of the network remarkably improves the performance of the network. In their study on training deep convolutional neural networks using huge image data, [PERSON] et al. [28] concluded that a very large network, such as ImageNet LSVRC-2010, is essential to achieving good results.