Reparametric Sampling for Small Object Image Recognition

Qiang Hua (), Pan Li (), Jiashuai Zhang (), Feng Zhang () and Chunru Dong
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Qiang Hua: Hebei Key Laboratory of Machine Learning and Computational Intelligence, Hebei University, Baoding 071002, P. R. China
Pan Li: Hebei Key Laboratory of Machine Learning and Computational Intelligence, Hebei University, Baoding 071002, P. R. China
Jiashuai Zhang: Hebei Key Laboratory of Machine Learning and Computational Intelligence, Hebei University, Baoding 071002, P. R. China
Feng Zhang: Hebei Key Laboratory of Machine Learning and Computational Intelligence, Hebei University, Baoding 071002, P. R. China
Chunru Dong: Hebei Key Laboratory of Machine Learning and Computational Intelligence, Hebei University, Baoding 071002, P. R. China

Asia-Pacific Journal of Operational Research (APJOR), 2023, vol. 40, issue 04, 1-18

Abstract: It is difficult to balance detection speed and detection accuracy in current methods for sampling on feature maps. We combine the parameterless interpolation algorithm used in the traditional method with the algorithm in deep learning to improve shortcomings of the traditional interpolation algorithm by introducing learnable parameters into the algorithm. The new algorithm guarantees the features of the original image by calculating the correlation between feature map channels and pixels to ensure the correct restoration of the image features during up-sampling. In this paper, we propose a new up-sampling operation model — Reparametric Blend Up-sampling (RBU), which uses the method of rearranging pixels to obtain a feature map that matches the up-sampling size, uses blending to compute inter-pixel and inter-channel correlations and back-propagates the model to learn the appropriate parameters to ensure feature extraction. In the field of object detection, this model replaces the original up-sampling operation and improves the mAP mean value by 1.1% and the detection of small target objects by 3.1% in the VOC dataset with the richest application scenarios.

Keywords: Deep learning; CNN; computer vision; up-sampling; attention mechanism (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S0217595923400092

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