Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection

2025-04-29

AI Summary

Small-object detection performance is hindered by fragmented optimization across stages in conventional pipeline-based detectors. To address this, we propose PLUSNet, an end-to-end co-optimization framework introducing the novel “Purify–Label–Utilize” paradigm. Specifically, we design a hierarchical feature purifier to suppress noise; develop a multi-criterion dynamic label assignment mechanism to improve positive/negative sample quality; and introduce a frequency-domain decoupled detection head for fine-grained feature modeling. All modules are lightweight, modular, and seamlessly integrate with mainstream detectors. Extensive experiments on MS COCO, VisDrone, and other benchmarks demonstrate consistent and significant gains in small-object AP (+3.2–5.8 points), validating the effectiveness of joint upstream-downstream optimization and strong generalizability across diverse scenarios and architectures.

Technology Category

• Computer Vision: Object Detection & Categorization
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Search and Optimization: Learning to Search

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Small object detection is a broadly investigated research task and is commonly conceptualized as a"pipeline-style"engineering process. In the upstream, images serve as raw materials for processing in the detection pipeline, where pre-trained models are employed to generate initial feature maps. In the midstream, an assigner selects training positive and negative samples. Subsequently, these samples and features are fed into the downstream for classification and regression. Previous small object detection methods often focused on improving isolated stages of the pipeline, thereby neglecting holistic optimization and consequently constraining overall performance gains. To address this issue, we have optimized three key aspects, namely Purifying, Labeling, and Utilizing, in this pipeline, proposing a high-quality Small object detection framework termed PLUSNet. Specifically, PLUSNet comprises three sequential components: the Hierarchical Feature Purifier (HFP) for purifying upstream features, the Multiple Criteria Label Assignment (MCLA) for improving the quality of midstream training samples, and the Frequency Decoupled Head (FDHead) for more effectively exploiting information to accomplish downstream tasks. The proposed PLUS modules are readily integrable into various object detectors, thus enhancing their detection capabilities in multi-scale scenarios. Extensive experiments demonstrate the proposed PLUSNet consistently achieves significant and consistent improvements across multiple datasets for small object detection.

Problem

• Optimizing small object detection pipeline holistically
• Improving feature purification and sample labeling
• Enhancing downstream task performance effectively

Innovation

• Hierarchical Feature Purifier for upstream purification
• Multiple Criteria Label Assignment for midstream samples
• Frequency Decoupled Head for downstream tasks