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Small Object Detection Network Based on Feature Information Enhancement.

Huilan Luo¹, Pei Wang¹, Hongkun Chen¹

¹School of Information Engineering, Jiangxi University of Science and Technology, Jiangxi, China.

Computational Intelligence and Neuroscience

|June 13, 2022

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This study introduces a novel network for enhanced small object detection, improving accuracy on benchmark datasets. The proposed method outperforms existing algorithms like YOLOv4 and DETR for detecting small objects.

Area of Science:

Computer Vision
Machine Learning
Artificial Intelligence

Background:

Existing object detection algorithms struggle with small objects due to their limited size and characteristics.
This limitation hinders performance in applications requiring precise identification of small-scale targets.

Purpose of the Study:

To propose a small object detection network that enhances feature information for improved detection accuracy.
To address the performance gap in detecting small objects compared to current state-of-the-art methods.

Main Methods:

Developed a novel small object detection network incorporating an information enhancement module.
Introduced a dense atrous convolution module to boost feature expression and discrimination.
Integrated these modules into existing architectures like RFBNet to assess generalizability.

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Main Results:

Achieved detection accuracies of 81.3% on PASCAL VOC, 34.8% on MS COCO, and 87.0% on UCAS-AOD datasets.
Demonstrated slight improvements over YOLOv4 (0.2%) and DETR (0.1%) in small object detection.
Showcased considerable performance gains when the proposed modules were integrated into other algorithms.

Conclusions:

The proposed feature information enhancement network effectively improves small object detection.
The novel modules offer a significant advancement for small object detection tasks.
The method's adaptability suggests broad applicability in various computer vision systems.