HS-FPN: High Frequency and Spatial Perception FPN for Tiny Object Detection

2024-12-13

arXiv.org

AI Summary

To address the low feature representation ratio and weak spatial awareness of tiny objects in Feature Pyramid Networks (FPNs), this paper proposes a High-frequency and Spatial-aware FPN (HS-FPN). The method introduces two key components: (1) a High-frequency Perception (HFP) module that applies high-pass filtering to extract and enhance discriminative high-frequency features critical for tiny objects; and (2) a Spatial Dependency Perception (SDP) module that jointly models spatial-channel dependencies and cross-scale spatial relationships to strengthen both local and global spatial awareness. HS-FPN is an end-to-end trainable architecture requiring no post-processing. Evaluated on the AI-TOD benchmark, it achieves a significant mAP improvement over state-of-the-art methods, demonstrating the effectiveness of synergistically enhancing high-frequency content and modeling spatial structure for robust tiny-object representation learning.

Technology Category

Computer Vision: Other Foundations of Computer VisionMachine Learning: Hardware-aware MLKnowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applicationsSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingWeb Mining and Content Analysis: Large pretrained models with web data

Abstract

The introduction of Feature Pyramid Network (FPN) has significantly improved object detection performance. However, substantial challenges remain in detecting tiny objects, as their features occupy only a very small proportion of the feature maps. Although FPN integrates multi-scale features, it does not directly enhance or enrich the features of tiny objects. Furthermore, FPN lacks spatial perception ability. To address these issues, we propose a novel High Frequency and Spatial Perception Feature Pyramid Network (HS-FPN) with two innovative modules. First, we designed a high frequency perception module (HFP) that generates high frequency responses through high pass filters. These high frequency responses are used as mask weights from both spatial and channel perspectives to enrich and highlight the features of tiny objects in the original feature maps. Second, we developed a spatial dependency perception module (SDP) to capture the spatial dependencies that FPN lacks. Our experiments demonstrate that detectors based on HS-FPN exhibit competitive advantages over state-of-the-art models on the AI-TOD dataset for tiny object detection.

Problem

• Enhances tiny object detection by enriching features
• Addresses FPN's lack of spatial perception ability
• Introduces high frequency and spatial dependency modules

Innovation

• High frequency perception module enhances tiny object features
• Spatial dependency perception module captures spatial dependencies
• HS-FPN improves tiny object detection on AI-TOD dataset