Composite Backbone Small Object Detection Based on Context and Multi-Scale Information with Attention Mechanism

Xinhan Jing, Xuesong Liu and Baolin Liu ()
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Xinhan Jing: School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
Xuesong Liu: School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
Baolin Liu: School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China

Mathematics, 2024, vol. 12, issue 5, 1-16

Abstract: Object detection has gained widespread application across various domains; nevertheless, small object detection still presents numerous challenges due to the inherent limitations of small objects, such as their limited resolution and susceptibility to interference from neighboring elements. To improve detection accuracy of small objects, this study presents a novel method that integrates context information, attention mechanism, and multi-scale information. First, to realize feature augmentation, a composite backbone network is employed which can jointly extract object features. On this basis, to efficiently incorporate context information and focus on key features, the composite dilated convolution and attention module (CDAM) is designed, consisting of a composite dilated convolution module (CDM) and convolutional block attention module (CBAM). Then, a feature elimination module (FEM) is introduced to reduce the feature proportion of medium and large objects on feature layers; the impact of neighboring objects on small object detection can thereby be mitigated. Experiments conducted on MS COCO validate the superior performance of the method compared with baseline detectors, while it yields an average enhancement of 0.8% in overall detection accuracy, with a notable enhancement of 2.7% in small object detection.

Keywords: small object detection; context information; composite backbone network; multi-scale information; attention mechanism (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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