Small Object Detection Algorithm Based on RPANet and Positional Convolution Attention Mechanism
Authors: 
Zongbing Tang, Dan Yang, Junsuo QuAuthors Info & Claims
Zongbing Tang, Dan Yang, Junsuo QuAuthors Info & ClaimsPages 338 - 344
Abstract
With the development of deep learning, small object detection has a significant role in application fields such as smart factories and remote sensing images. In order to address the problem of difficult and low accuracy detection of small objects due to small pixel scale and little feature information. In this paper, we present a path aggregation network with residual characteristic RPANet on YOLOv3 algorithm, which can twice use the feature information of the backbone network to enhance the small object feature information, and also offer a positional convolution attention mechanism module PCAM to thoroughly learn and extract the small object feature information as well as reduce the unnecessary feature information in the background, so as to further enhance the detection capability of the model for small objects. The experimental results demonstrate that the improved YOLOv3 algorithm is more effective for small object detection.
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Index Terms
- Small Object Detection Algorithm Based on RPANet and Positional Convolution Attention Mechanism
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Published In
September 2022
1221 pages
ISBN:9781450396899
DOI:10.1145/3573942
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Published: 16 May 2023
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AIPR 2022
AIPR 2022: 2022 5th International Conference on Artificial Intelligence and Pattern Recognition
September 23 - 25, 2022
Xiamen, China
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