Object detection based on deep learning

Object detection is a hot topic in the field of computer vision and pattern recognition. The task of object detection is to accurately and efficiently identify and locate many object instances of predefined categories from images. With the wide application of deep learning, the accuracy and efficiency of object detection have been greatly improved. However, object detection based on deep learning still faces challenges such as improving the performance of mainstream object detection algorithms and the detection accuracy of small target objects. In this paper, based on extensive literature research, we survey the mainstream algorithms of object detection from the angle of improving and optimizing the two-stage and onestage object detection algorithms. We also analyze the promotion method of small object detection accuracy combined with the backbone network, the visual receptive field, and the model's training. In addition, the common data sets of object detection are introduced in detail, while the performance of representative algorithms is compared from two aspects. The problems to be solved in object detection and the future research direction are predicted and prospected. More high precision and efficient algorithms are proposed, and more research directions will be developed in the future.