## 1 Introduction  
Recently, computer vision has been extensively researched in the area of object detection for industrial automation, consumer electronics, medical imaging, military, and video surveillance. It is predicted that the computer vision market will be worth $50 billion by the end of 2020. For object recognition, the raw input data are represented in matrix pixel form, where the first representation layer abstracts the pixels and encodes edges, the next layer composes and encodes edge arrangement, the next layer up encodes eyes and noses, and the final layer recognizes a face present in the image. Normally, a deep learning process optimally classifies the facial features into their respective levels without supervision. In object classification application, manual feature extraction is eliminated by a convolutional neural network (CNN), so there is no need to manually identify features that are useful for image classification. CNNs extract features directly from images, and these extracted features are not pre-trained, but they learn while the network is trained on collected images. Due to automatic feature extraction, deep learning models became highly accurate in computer vision. Deep CNN architecture involves complex models. They require large image datasets for higher accuracy. CNNs require large labeled datasets to perform related tasks in computer vision, such as object classification, detection, object tracking, and recognition. With the advancement in technology and the availability of powerful graphics processing unit's (GPU), deep learning has been employed on datasets; state-of-the-art results have been demonstrated by researchers in areas such as object classification, detection, and recognition. To perform both training and testing, deep learning requires powerful computational resources and larger datasets. In computer vision, image classification is the most widely researched area and it has attained astonishing results in worldwide competitions through PASCAL, ILSVRC, VOC, and MS-COCO, which apply deep learning techniques [1]. Deep learning techniques are deployed for object detection due to promising results in image classification [2]. [PERSON] et al. [3] implemented classification of sonar images with various added noises on GoogleNet CNN and tested on TDI 2017 and 2018 datasets. In generic object detection, the main aim is to determine whether or not there are any instances of objects from the specified varieties (e.g., animals, vehicles, and pedestrians) in an image, and if present, then return the spatial location and extent of a single object (by bounding box) [4; 5]. Object detection became the basis for solving overly complex vision-related tasks; namely, scene understanding, image captioning, instance segmentation, semantic segmentation, object recognition, and tracking [6; 7]. The applications of object detection cover areas such as Internet of Things (IoT) and artificial intelligence, which includes intelligent military surveillance systems, security, self-driving cars, robot vision, human-computer interaction (HCI), and consumer electronics. Recently, deep learning methods [8; 9] have emerged as the most powerful techniques for automatically learning features from raw data. Specifically, deep learning methods have achieved great progress in object detection, a problem that has grabbed the attention of many researchers in this decade. Video surveillance is one of the most challenging and fundamental areas in security systems, as it depends entirely on a lot on object detection and tracking. It monitors the behavior of people in public to detect any suspicious behavior [10]. The road-map of object detection milestones is shown in Figure 1.  
Figure 1: Milestones of object detection. In 2012 the major turning point was the use of DCNN implemented for image classification by [PERSON] et al. [1], VJ Det. [11; 12], HOG Det. [13], DPM [14; 15; 16], RCNN [17], etc. (source: [18]).