Small Object Detection Method Based on Global Multi-Level Perception and Dynamic Region Aggregation

In the field of object detection, detecting small objects is an important and challenging task. However, most existing methods tend to focus on designing complex network structures, lack attention to global representation, and ignore redundant noise and dense distribution of small objects in complex...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems for video technology 2024-10, Vol.34 (10), p.10011-10022
Main Authors: Zhu, Zhiqin, Zheng, Renzhong, Qi, Guanqiu, Li, Shuang, Li, Yuanyuan, Gao, Xinbo
Format: Article
Language:English
Subjects:
Attention
Data augmentation
Detectors
dynamic region aggregation
Feature extraction
global multi-level perception
Linear transformations
Modules
Noise
Object detection
Object recognition
Perception
Semantics
Small object
sparse strategy
Task analysis
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Summary:In the field of object detection, detecting small objects is an important and challenging task. However, most existing methods tend to focus on designing complex network structures, lack attention to global representation, and ignore redundant noise and dense distribution of small objects in complex networks. To address the above problems, this paper proposes a small object detection method based on global multi-level perception and dynamic region aggregation. The method achieves accurate detection by dynamically aggregating effective features within a region while fully perceiving the features. This method mainly consists of two modules: global multi-level perception module and dynamic region aggregation module. In the global multi-level perception module, self-attention is used to perceive the global region, and its linear transformation is mapped through a convolutional network to increase the local details of global perception, thereby obtaining more refined global information. The dynamic region aggregation module, devised with a sparse strategy in mind, selectively interacts with relevant features. This design allows aggregation of key features of individual instances, effectively mitigating noise interference. Consequently, this approach addresses the challenges associated with densely distributed targets and enhances the model's ability to discriminate on a fine-grained level. This proposed method was evaluated on two popular datasets. Experimental results show that this method outperforms state-of-the-art methods in small object detection tasks, demonstrating good performance and potential applications.
ISSN:1051-8215
1558-2205
DOI:10.1109/TCSVT.2024.3402097