###### Abstract  
Deep learning is a relatively new branch of machine learning in which computers are taught to recognize patterns in massive volumes of data. It primarily describes learning at various levels of representation, which aids in understanding data that includes text, voice, and visuals. Convolutional neural networks have been used to solve challenges in computer vision, including object identification, image classification, semantic segmentation and a lot more. Object detection in videos involves confirming the presence of the object in the image or video and then locating it accurately for recognition. In the video, modelling techniques suffer from high computation and memory costs, which may decrease performance measures such as accuracy and efficiency to identify the object accurately in real-time. The current object detection technique based on a deep convolution neural network requires executing multilevel convolution and pooling operations on the entire image to extract deep semantic properties from it. For large objects, detection models can provide superior results; however, those models fail to detect the varying size of the objects that have low resolution and are greatly influenced by noise because the features after the repeated convolution operations of existing models do not fully represent the essential characteristics of the objects in real-time. With the help of a multi-scale anchor box, the proposed approach reported in this paper enhances the detection accuracy by extracting features at multiple convolution levels of the object. The major contribution of this paper is to design a model to understand better the parameters and the hyper-parameters which affect the detection and the recognition of objects of varying sizes and shapes, and to achieve real-time object detection and recognition speeds by improving accuracy. The proposed model has achieved 84.49 mAP on the test set of the Pascal VOC-2007 dataset at 11 FPS, which is comparatively better than other real-time object detection models. deep learning; convolution neural network; object detection and recognition; PASCAL VOC dataset; FDDB dataset +
Footnote †: journal: _Future Internet_  
to re-train the model for various other applications. Here, the term parameters include: the number of hidden layers, type of layers, training epochs, and many more. Deep learning has revolutionized the developments in image processing, natural language processing, biology, autonomous driving, artificial intelligence, and more [1]. Deep learning is regarded as the subset of machine learning that uses a hierarchical level of layers to carry out machine learning [2]. Traditionally, data analysis was performed linearly; however, with the advent of deep learning, machines can now process data in a nonlinear approach [3]. This paper focuses on object detection and recognition. The challenging task at the initial stage is to distinguish between the related computer vision tasks. For example, it might be difficult to differentiate between image classifications, object localization, and object detection. Object detection involves both a bounding box around an image and assigning a class label. Altogether it is referred to as object recognition. The past work that has been undertaken regarding object detection involves the extraction of the features by using algorithms like HOG [4], SIFT [5], and SURF [6]. These algorithms use the traditional machine learning approaches, i.e., first performing feature extraction and then training the algorithm to achieve the desired output; however, deep learning algorithms have shown a significant advantage over the traditional machine learning approach by training the algorithm from the data itself. No features are extracted manually. The deep learning algorithm differs as in it a network is used to extract features along with bounding box prediction, etc. As a result of this, a faster and more accurate object detection system is obtained.