Few-shot object detection based on one-stage detector and meta-learning

Dong Liang; Zhe Li; Honghan Qin; Zhibin Chen; Cong Huang; Xiao Huang; Jiang Li

doi:10.1117/12.3009290

11 October 2023 Few-shot object detection based on one-stage detector and meta-learning

Dong Liang, Zhe Li, Honghan Qin, Zhibin Chen, Cong Huang, Xiao Huang, Jiang Li

Author Affiliations +

Proceedings Volume 12918, Fourth International Conference on Computer Science and Communication Technology (ICCSCT 2023); 1291826 (2023) https://doi.org/10.1117/12.3009290
Event: International Conference on Computer Science and Communication Technology (ICCSCT 2023), 2023, Wuhan, China

Abstract

There has been rapid progress in the development of object detection methods. A challenging problem of deep neural networks based object detection algorithms is they rely heavily on abundant training samples. When dealing with scenarios where samples are scarce, the accuracy of these models can degrade sharply, thus hampering their final efficacy. Recently, the emergence of few-shot detection methods has addressed this issue. The majority of existing research in the literature concentrates on constructing few-shot detection models based on two-stage object detectors due to their high capacity and compatibility with affiliated structures. As a result, these methods are capable of delivering high-performance object detection with a limited number of training samples. However, two-stage backbones often imply a lower inference efficiency as well as applicability with deployment on edge devices. To realize high-speed object detection specifically in computation resource-constrained scenarios, we propose a one-stage few-shot object detector by integrating a meta-learning structure with YOLOv3 model. Experimental results demonstrate that our proposed few-shot object detector achieves comparable detection accuracy to existing two-stage object detectors under the same training conditions. Meanwhile, it significantly prompts the inference speed, enabling real-time object detection in videos.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Dong Liang, Zhe Li, Honghan Qin, Zhibin Chen, Cong Huang, Xiao Huang, and Jiang Li "Few-shot object detection based on one-stage detector and meta-learning", Proc. SPIE 12918, Fourth International Conference on Computer Science and Communication Technology (ICCSCT 2023), 1291826 (11 October 2023); https://doi.org/10.1117/12.3009290

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KEYWORDS

Object detection

Education and training

Machine learning

RGB color model

Image classification

Statistical modeling

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