• Consistent and comprehensive s...

Treffer: Consistent and comprehensive scale aggregation network for drone-view small object detection.

Title:
Consistent and comprehensive scale aggregation network for drone-view small object detection.
Authors:
Zhang F; School of Electronic Engineering, Xidian University, Xi'an, 710071, China; Key Laboratory of Intelligent Spectrum Sensing and Information Fusion, Xi'an, 710071, China. Electronic address: fan_zhang@stu.xidian.edu.cn., Ji H; School of Electronic Engineering, Xidian University, Xi'an, 710071, China; Key Laboratory of Intelligent Spectrum Sensing and Information Fusion, Xi'an, 710071, China. Electronic address: hbji@xidian.edu.cn., Zhang Y; School of Electronic Engineering, Xidian University, Xi'an, 710071, China; Key Laboratory of Intelligent Spectrum Sensing and Information Fusion, Xi'an, 710071, China. Electronic address: zhangyq@xidian.edu.cn., Su Z; School of Computer Science and Technology, Xidian University, Xi'an, 710071, China; Key Laboratory of Intelligent Spectrum Sensing and Information Fusion, Xi'an, 710071, China. Electronic address: zzsu@xidian.edu.cn.
Source:
Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2026 Mar; Vol. 195, pp. 108250. Date of Electronic Publication: 2025 Oct 28.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Pergamon Press Country of Publication: United States NLM ID: 8805018 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-2782 (Electronic) Linking ISSN: 08936080 NLM ISO Abbreviation: Neural Netw Subsets: MEDLINE
Imprint Name(s):
Original Publication: New York : Pergamon Press, [c1988-
MeSH Terms:
Robotics*/methods , Pattern Recognition, Automated*/methods , Neural Networks, Computer*, Humans ; Algorithms
Contributed Indexing:
Keywords: Drone-view object detection; Feature pyramid network; Full-scale interaction; Small objects; Spatial calibration
Entry Date(s):
Date Created: 20251103 Date Completed: 20260124 Latest Revision: 20260128
Update Code:
20260130
DOI:
10.1016/j.neunet.2025.108250
PMID:
41183438
Database:
MEDLINE

Weitere Informationen

Accurate detection in UAV scenarios is challenging due to the small size of objects. This is attributed to two non-negligible factors: (1) small objects lack sufficient visual cues and often suffer information loss during feature extraction; (2) the positional sensitivity of small objects complicates the optimization of predicted bounding boxes. To address these issues, we present a Consistent and Comprehensive Scale Aggregation Network (C<sup>2</sup>SANet). For high-quality feature representations of small objects, C<sup>2</sup>SANet develops a novel Multi-Scale Interactive Feature Pyramid Network (MSI-FPN), which introduces two new components based on the top-down propagation path: the Deformable-Based Spatial Calibration (DSC) and Scale Feature Enhancement (SFE) modules. Specifically, DSC leverages pixel-spatial information between adjacent scale features to adjust up-sampled deep features, enhancing the consistency of semantic propagation. SFE first unifies the spatial size of all scale features, then achieves the "Collect-and-Distribute" of full-scale information through the scale interaction block with an encoder-decoder structure, ensuring that the shallow features can be complemented with comprehensive semantic information. Additionally, to improve the localization prediction of small objects, a Coarse-to-Fine Detection Head (CFDH) with geometrical-aware adjustment is devised to refine the quality of predicted boxes iteratively. Extensive experiment results demonstrate the effectiveness and generalizability of C<sup>2</sup>SANet in improving small object detection performance.
(Copyright © 2025 Elsevier Ltd. All rights reserved.)

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.