🤖 AI Summary
Single-stage detectors struggle with small-object detection due to semantically impoverished shallow features and low-resolution deep features. To address this, we propose a “cross-layer feature borrowing” paradigm: discriminative deep semantics from large, same-category objects are identified via feature matching, then dynamically integrated into shallow feature maps through weighted aggregation and context-aware fusion—enhancing small-object representations without compromising inference speed or breaking the resolution–semantics trade-off. Our method is built upon the SSD framework and supports end-to-end differentiable training. On the COCO benchmark, it achieves a +4.2% improvement in AP for small objects while maintaining real-time inference speed, demonstrating robustness and generalization across complex scenes.
📝 Abstract
Detecting small objects remains a significant challenge in single-shot object detectors due to the inherent trade-off between spatial resolution and semantic richness in convolutional feature maps. To address this issue, we propose a novel framework that enables small object representations to"borrow"discriminative features from larger, semantically richer instances within the same class. Our architecture introduces three key components: the Feature Matching Block (FMB) to identify semantically similar descriptors across layers, the Feature Representing Block (FRB) to generate enhanced shallow features through weighted aggregation, and the Feature Fusion Block (FFB) to refine feature maps by integrating original, borrowed, and context information. Built upon the SSD framework, our method improves the descriptive capacity of shallow layers while maintaining real-time detection performance. Experimental results demonstrate that our approach significantly boosts small object detection accuracy over baseline methods, offering a promising direction for robust object detection in complex visual environments.