Boundary and Position Information Mining for Aerial Small Object Detection

2026-01-23

AI Summary

This work addresses the challenge of detecting small objects in drone-captured aerial images, which suffer from significant scale variation and blurred boundaries. To tackle this issue, the authors propose a Boundary and Position Information Mining (BPIM) framework that integrates Position Information Guidance (PIG), Boundary Information Guidance (BIG), Cross-Scale Fusion (CSF), and Triple Feature Fusion (TFF), complemented by an Adaptive Weight Fusion (AWF) mechanism. This design effectively enhances contextual awareness and discriminative capability for small objects. Evaluated on the VisDrone2021, DOTA1.0, and WiderPerson benchmarks, BPIM substantially outperforms the YOLOv5-P2 baseline, achieving state-of-the-art performance while maintaining manageable computational overhead.

Technology Category

• Computer Vision: Object Detection & Categorization
• Machine Learning: Multi-instance/Multi-view Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

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

Abstract

Unmanned Aerial Vehicle (UAV) applications have become increasingly prevalent in aerial photography and object recognition. However, there are major challenges to accurately capturing small targets in object detection due to the imbalanced scale and the blurred edges. To address these issues, boundary and position information mining (BPIM) framework is proposed for capturing object edge and location cues. The proposed BPIM includes position information guidance (PIG) module for obtaining location information, boundary information guidance (BIG) module for extracting object edge, cross scale fusion (CSF) module for gradually assembling the shallow layer image feature, three feature fusion (TFF) module for progressively combining position and boundary information, and adaptive weight fusion (AWF) module for flexibly merging the deep layer semantic feature. Therefore, BPIM can integrate boundary, position, and scale information in image for small object detection using attention mechanisms and cross-scale feature fusion strategies. Furthermore, BPIM not only improves the discrimination of the contextual feature by adaptive weight fusion with boundary, but also enhances small object perceptions by cross-scale position fusion. On the VisDrone2021, DOTA1.0, and WiderPerson datasets, experimental results show the better performances of BPIM compared to the baseline Yolov5-P2, and obtains the promising performance in the state-of-the-art methods with comparable computation load.

Problem

• small object detection
• scale imbalance
• blurred edges
• aerial imagery
• UAV

Innovation

• Boundary Information Mining
• Position Information Guidance
• Cross-Scale Feature Fusion
• Small Object Detection
• Adaptive Weight Fusion