Deep learning based object detection for resource constrained devices: Systematic review, future trends and challenges ahead

Deep learning models are widely being employed for object detection due to their high performance. However, the majority of applications that require object detection are functioning on resource-constrained edge devices. In the present era, there is a need for deep learning-based object detectors that are lightweight and perform well on these constrained edge devices. Objective: The research aims to identify current trends in resource-constrained applications for deep learning-based object detectors in terms of the technique used to create the model, the type of input image involved, the type of device used, and the type of application addressed by the model. Method: To achieve the objective of our research, a systematic literature review was carried out that yielded 167 studies. The models or techniques employed in the studies were grouped to better understand the research problem at hand. This review carefully reports every decision and provides many visualizations of the final studies in order to draw clear conclusions. Conclusion: The conclusion discussed the gaps, possibilities, and future perspectives discovered throughout the research process, implying that this field of study has grown profoundly in the last decade.