| Summary: | With the development of deep learning, object detection technology has gradually changed from traditional manual detection methods to deep neural network detection methods. Among many object detection algorithms based on deep learning, the one-stage object detection method based on deep learning is widely used because of its simple network structure, fast running speed and higher detection efficiency. However, the existing one-stage object detection methods based on deep learning do not have ideal detection results for small target objects in the detection process due to the lack of feature information, low resolution, complicated background information, unobvious details and higher positioning accuracy, which reduces the detection accuracy of the model. Aiming at the existing problems of one-stage object detection method based on deep learning, a large amount of one-stage small object detection technologies based on deep learning are studied. Firstly, the optimization methods for small object detection are systematically summarized from the aspects of Anchor Box, network structure, IoU (intersection over union) and loss function in the one-stage object detection methods. Secondly, the commonly used small object detection datasets and their application fields are listed, and the detection graphs on each small object detection dataset are given. Finally, the future research direction of one-stage small object detection methods based on deep learning is investigated.
|
|---|