A Review of Research on Small Object Detection Algorithms Based on Deep Learning
DOI:
https://doi.org/10.53469/jtpms.2025.05(4).04Keywords:
Small target detection, Deep learning, Feature enhancement, Multi-scale features, Attention mechanismAbstract
Small object detection is an important research direction in the field of computer vision and one of the most challenging problems in object detection tasks. With the rapid development of deep learning technology, object detection algorithms based on deep neural networks have achieved significant results in large and medium-sized object detection, but still face many challenges in small object detection. This article systematically reviews the research status and development trends of small object detection algorithms based on deep learning in recent years. Firstly, the concept definition and main challenges of small object detection were introduced, and the limitations of traditional object detection algorithms in small object scenarios were analyzed. Then, the focus was on the classification and discussion of small object detection methods based on deep learning, including key technologies such as feature enhancement, multi-scale feature fusion, and attention mechanism. At the same time, in-depth analysis and comparison were conducted on the optimization and improvement schemes of mainstream detection algorithms such as YOLOv5/v7 and SSD in small object detection. Finally, the existing problems in current research were summarized, and future development directions were discussed.
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