CEM-FBGTinyDet: Context-Enhanced Foreground Balance with Gradient Tuning for tiny Objects

2025-06-11

AI Summary

In tiny object detection, standard label assignment causes positive sample deficiency in high-level feature pyramid layers (P5–P6), leading to gradient stagnation and semantic degradation; meanwhile, low-level features lack high-level semantic guidance, impairing classification robustness. To address this, we propose E-FPN-BS—a novel framework introducing higher-order feature reuse. It comprises a Context Enhancement Module (CEM) for cross-scale semantic activation, a Foreground-Background Separation Module (FBSM) to improve localization discriminability, and a Scale-Aware Dynamic Gradient Balancing Loss (DCLoss) ensuring equitable gradient backpropagation. Leveraging multi-branch alignment, spatially gated masking, and adaptive weighted fusion, E-FPN-BS achieves consistent mAP gains of +3.2–5.8 across multiple benchmarks, while maintaining strong generalization and real-time deployability.

Technology Category

• Computer Vision: Object Detection & Categorization
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Tiny object detection (TOD) reveals a fundamental flaw in feature pyramid networks: high-level features (P5-P6) frequently receive zero positive anchors under standard label assignment protocols, leaving their semantic representations untrained due to exclusion from loss computation. This creates dual deficiencies: (1) Stranded high-level features become semantic dead-ends without gradient updates, while (2) low-level features lack essential semantic context for robust classification. We propose E-FPN-BS that systematically converts wasted high-level semantics into low-level feature enhancements. To address these issues, we propose E-FPN-BS, a novel architecture integrating multi-scale feature enhancement and adaptive optimization. First, our Context Enhancement Module(CEM) employs dual-branch processing to align and compress high-level features for effective global-local fusion. Second, the Foreground-Background Separation Module (FBSM) generates spatial gating masks that dynamically amplify discriminative regions. To address gradient imbalance across object scales, we further propose a Dynamic Gradient-Balanced Loss (DCLoss) that automatically modulates loss contributions via scale-aware gradient equilibrium. Extensive experiments across multiple benchmark datasets demonstrate the outstanding performance and generalization ability of our approach.

Problem

• High-level features lack training due to zero positive anchors
• Low-level features miss semantic context for classification
• Gradient imbalance across object scales affects detection performance

Innovation

• Context Enhancement Module aligns high-level features
• Foreground-Background Separation Module amplifies regions
• Dynamic Gradient-Balanced Loss modulates contributions