Few-shot object detection algorithm based on improved faster R-CNN

Zhipeng Zheng; Anjun Song

doi:10.1117/12.3033534

13 June 2024 Few-shot object detection algorithm based on improved faster R-CNN

Zhipeng Zheng, Anjun Song

Proceedings Volume 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024); 131803N (2024) https://doi.org/10.1117/12.3033534
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 2024, Guangzhou, China

Abstract

The task of object detection requires extensive, high-quality image datasets to acquire the ability to recognize objects in images and determine their positions. By utilizing rich and diverse datasets, model can achieve better generalization capability and mitigate overfitting issue. However, in real-world industrial settings, annotating datasets requires a significant investment of human and material resources. Addressing these challenges, this paper proposes a few-shot object detection algorithm (SFA-FSOD) based on Faster R-CNN. Firstly, the SimAM-FPN module is proposed, which improves the quality of the Region Proposal Network in locating Regions of Interest by integrating multi-scale features and attention mechanisms into the feature pyramid network, thereby reducing the number of irrelevant candidate boxes. Secondly, the Feature Aggregation (FA) module based on prototypical networks is proposed, aggregating crucial regions around instances to generate basic feature for each category. Finally, Euclidean distance is employed to select and fuse the most similar basic feature from the feature prototype library, thereby enhancing weak features in the few-shot object detection task. Extensive experiments on the Pascal VOC and MSCOCO datasets validate the effectiveness of SFA-FSOD.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhipeng Zheng and Anjun Song "Few-shot object detection algorithm based on improved faster R-CNN", Proc. SPIE 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 131803N (13 June 2024); https://doi.org/10.1117/12.3033534

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
6 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Object detection

Prototyping

Detection and tracking algorithms

Education and training

Machine learning

Feature extraction

Ablation

Show All Keywords

Keywords/Phrases

Search In:

Publication Years