Object detection is one of the most fundamental and challenging tasks to locate objects in images and videos. Over the past, it has gained much attention to do more research on computer vision tasks such as object classification, counting of objects, and object monitoring. This study provides a detailed literature review focusing on object detection and discusses the object detection techniques. A systematic review has been followed to summarize the current research work's findings and discuss seven research questions related to object detection. Our contribution to the current research work is (i) analysis of traditional, two-stage, one-stage object detection techniques, (ii) Dataset preparation and available standard dataset, (iii) Annotation tools, and (iv) performance evaluation metrics. In addition, a comparative analysis has been performed and analyzed that the proposed techniques are different in their architecture, optimization function, and training strategies. With the remarkable success of deep neural networks in object detection, the performance of the detectors has improved. Various research challenges and future directions for object detection also has been discussed in this research paper.

Abstract Cloud computing has gained attention due to its sophisticated processing architecture and data storage capabilities in the last couple of years. Due to high volume data and the endless number of possible users, the security of the account holder's stored data and privacy becomes essential for this paradigm. This article focuses on the encryption architecture of data storage and retrieval by creating an encrypted searchable index, which is inspired by symmetric searchable encryption. Ranking becomes a need for providing the best out of the search results to the user. This research article proposes an efficient and flexible artificial neural network (ANN) based ranking scheme to search for documents from the cloud server. The proposed algorithm architecture is segmented into three parts. The first part is the generation of the encryption index over the uploaded data, the second part is query analysis, and the third part is ranking. To consolidate the encryption mechanism, RSA, NTRU, and AES were used based on the requirement of the data. To orient the retrieval part, the degree of the top keyword in the server is determined by using term frequency with inverse document frequency schemes. The retrieved documents are further ranked using ANNs. The simulation setup was done on MATLAB 2016b having datasets from Kaggle (Twitter data) and FIRE dataset.