School of Physics and Electronic Engineering, Yancheng Teachers University, Yancheng, Jiangsu 224007, China.
Comput Intell Neurosci. 2022 Jul 14;2022:3843155. doi: 10.1155/2022/3843155. eCollection 2022.
Compared with the traditional object detection algorithm, the object detection algorithm based on deep learning has stronger robustness to complex scenarios, which is the hot direction of current research. According to the process characteristics of the object detection algorithm based on deep learning, it is divided into two-stage object detection algorithm and single-stage object detection algorithm, focusing on the problems solved by some classical algorithms and their advantages and disadvantages. In view of the problem of object detection, especially small object detection, the commonly used data sets and performance evaluation indicators are summarized; the characteristics, advantages, and detection difficulties of various common data sets are compared; the challenges faced by commonly used object detection methods and small object detection are systematically summarized; the latest work of small object detection methods based on deep learning is sorted out; and the small object detection methods based on multiscale and small object detection methods based on super-resolution are introduced. At the same time, the lightweight strategy for target detection methods and the performance of some lightweight models are introduced; the characteristics, advantages, and limitations of various methods are summarized; and the future development direction of small object detection methods based on deep learning is prospected.
与传统的目标检测算法相比，基于深度学习的目标检测算法对复杂场景具有更强的鲁棒性，是当前研究的热点方向。根据基于深度学习的目标检测算法的过程特点，将其分为两阶段目标检测算法和单阶段目标检测算法，重点介绍了一些经典算法解决的问题及其优缺点。针对目标检测，特别是小目标检测问题，总结了常用的数据集和性能评估指标；比较了各种常见数据集的特点、优点和检测难点；系统总结了常用目标检测方法和小目标检测所面临的挑战；对基于深度学习的小目标检测方法的最新工作进行了梳理；介绍了基于多尺度的小目标检测方法和基于超分辨率的小目标检测方法。同时，介绍了目标检测方法的轻量化策略和一些轻量化模型的性能；总结了各种方法的特点、优点和局限性；并展望了基于深度学习的小目标检测方法的未来发展方向。
