A novel few-shot object detection framework for multi-scene driving based on contrastive proposal encoding
Year: 2025
Type: article
Abstract: This paper proposes a few-shot object detection algorithm based on FSCE, tailored for multi-scenario driving environments. Unlike existing methods that focus on single scenarios, our approach addresses challenges of cross-scenario heterogeneity and overfitting in low-data regimes. We enhance feature representation through a multi-scale feature module that integrates local and contextual information, and replace the traditional Softmax with a cosine Softmax classifier to reduce intra-class variance via L2 normalization and angular margin constraints. This work is the first to apply few-shot detection to both nighttime infrared and daytime visible-light driving scenarios. Experiments on FLIR and BDD100K demonstrate superior generalization and accuracy over existing methods. Future work will explore reducing model complexity while maintaining performance.
Source: PLoS ONE
Authors: Yalei Dong, Jing Xiao, Fengchen Wei
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,382
OpenAlex ID: W7117561852