Researches advanced in object detection based on deep learning

Object detection is a popular direction in the field of computer research, and the main task is to accurately classify and localize object instances in images. Since the advent of deep learning technology, it has advanced significantly in both speed and accuracy. However, there is still potential for improvement in the areas of multi-scale object detection, weakly supervised object detection, and unsupervised object detection. In this research, we examine and describe the current mainstream object identification algorithms based on deep learning from the viewpoints of process, network structure, common data sets, and model training approaches, based on detailed reference to a vast quantity of existing literature. We also give improvement suggestions according to the main problems and look forward to the future research focus in the field of object detection.