A systematic review of object detection from images using deep learning

The development of object detection has led to huge improvements in human interaction systems. Object detection is a challenging task because it involves many parameters including variations in poses, resolution, occlusion, and daytime versus nighttime detection. This study surveys on various aspects of object detection that includes (1) basics of object detection, (2) object detection techniques, (3) datasets, (4) metrics and deep learning libraries. This study presents a systematic analysis of recent publications on object detection covering around 400 research articles and synthesised the findings to provide empirical answers to research questions. The review is based on relevant articles published from 2015 through 2022, as well as discussions of challenges and future directions in this field. Furthermore, the survey examined the contributions of various researchers concerning their respective application domains, while emphasizing the advantages and disadvantages of the research work. Despite the success of various methods proposed in literature for predicting results, there remains room for improvement in the accuracy of object detection.