%0 Journal Article %A FU Miaomiao %A DENG Miaolei %A ZHANG Dexian %T Object Detection Algorithms Based on Deep Learning and Transformer %D 2023 %R 10.3778/j.issn.1002-8331.2205-0354 %J Computer Engineering and Applications %P 37-48 %V 59 %N 1 %X Object detection is the basis for advanced vision tasks such as object tracking and instance segmentation, and has important applications in real-world scenarios such as intelligent transportation, defect detection, and intelligent security. Existing high-precision detection algorithms are all implemented under the guidance of deep learning, accompanied by Anchor frame technology. However, the shortcomings of the anchor frame itself have a great impact on the performance of the detector. Anchor-free collision detection has become a target detection method in recent years. new research directions in the field. At the same time, the great potential shown by Transformer has opened up a new direction of combining image and Transformer for the field of vision, and Transformer-based target detection has also become a new research hotspot. This paper systematically summarizes the target detection algorithms in the deep learning era, investigates and studies related papers on target detection in the past five years, focuses on in-depth analysis of these algorithms from the perspectives of Anchor-free and Transformer, and introduces the specific application situation of these algorithms in real scenarios and the commonly used datasets in the field of target detection. Finally, based on the current research status, the future research directions of target detection are prospected. %U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2205-0354