Starting from the structure: A review of small object detection based on deep learning

Object detection, as one of the most fundamental and essential tasks in the field of computer vision, has been the focus of unremitting efforts by researchers, who are committed to modifying the neural network structure in order to improve the accuracy of object detection and expedite task execution. As the application scope continues to expand, small object detection has gradually emerged as a crucial branch in the field of object detection. In this paper, the development history of object detection algorithms is introduced, the concept of small objects is introduced, and the current problems and challenges faced by small object detection are outlined. In this paper, the network structure is disassembled from a macroscopic point of view, and improved algorithms such as enhanced data augmentation, improved feature extraction, superior feature fusion, and refined loss functions are described in detail. Furthermore, the paper explores a series of emerging and improved algorithms for small object detection. It encompasses the introduction of advanced strategies such as unsupervised learning, end-to-end training, density cropping, transfer learning, and anchor-free approaches. The paper provides a comprehensive list of commonly used general-purpose datasets and domain-specific datasets for small object detection tasks, offering performance comparisons of the mentioned improved algorithms. In conclusion, the paper summarizes and provides an outlook on current small object detection algorithms, furnishing the reader with a thorough understanding of the field and insights into future directions.