Aug 2026· International Conference on Computer Vision and Information Technology· Vol 14321, pp. 1432102 - 1432102-10· 0 citations· 40 references
Engineering
TL;DR
SSM-YOLO11s is proposed, a lightweight model optimized for small object detection in aerial imagery that achieves a superior balance between precision and efficiency compared to state-of-the-art models.
Abstract
Unmanned Aerial Vehicle (UAV) aerial photography is extensively utilized in security, traffic monitoring, and disaster rescue. However, UAV-captured images present significant challenges, including small target scales, dense distribution, and complex backgrounds. While conventional object detection algorithms like the YOLO series have made progress, they often struggle to balance accuracy and real-time performance in these resource-constrained environments. To address these issues, we propose SSM-YOLO11s, a lightweight model optimized for small object detection in aerial imagery. Our approach first introduces the Sitou module, which employs a deep-channel compression and shallow feature retention strategy with a secondary fusion branch to reduce parameters by 50% while enhancing fine-grained feature utilization. Furthermore, the lightweight SNGSConvE module is designed by integrating SNI, GSConvE, and CSPOmniKernel to mitigate feature misalignment and strengthen capture capabilities. Finally, a Multi-Scale Edge Enhancement (MSEE) module is constructed to fuse edge details across multiple scales, improving target discriminability. Experimental results on the VisDrone2019 dataset demonstrate that SSM-YOLO11s achieves a superior balance between precision and efficiency compared to state-of-the-art models.
Object detection in UAV aerial imagery plays a vital role in applications such as traffic surveillance, urban management, and low-altitude inspection. However, aerial images typically present challenges including small object scales, dense distributions, severe occlusion, and cluttered backgrounds. Existing YOLO-series detectors still exhibit limitations in small-object feature representation, multi-scale contextual modeling, and downsampling detail preservation. To address these issues, this paper proposes MD-YOLO, an improved object detection model tailored for UAV scenarios, built upon the YOLO26 baseline. MD-YOLO incorporates three lightweight modules—IMO, DS-SPPF, and HPConv to optimize backbone feature extraction, multi-scale contextual aggregation, and Neck downsampling, respectively, thereby enhancing the model’s detection capability for small objects in complex UAV scenarios. Experimental results demonstrate that, compared with the baseline model, MD-YOLO achieves improvements of 3.9, 3.2, 2.9, and 3.1 percentage points in Precision, Recall, mAP50, and mAP50-95 on the VisDrone-2019 dataset, while maintaining a parameter count of only 9.3 M, thereby striking a favorable balance between accuracy and complexity. Independent evaluations on the UAVDT and NWPU VHR-10 datasets further confirm the consistent effectiveness of the model across diverse UAV imaging conditions, with mAP50 improvements of 8.3 and 1.4 percentage points, respectively.
Detecting small UAV targets in air-to-air scenarios is important for aerial security, autonomous surveillance, and multi-UAV defense systems. However, this task remains challenging due to severe scale variation, motion blur, and complex aerial backgrounds. Existing detectors suffer from performance degradation on extremely small targets while maintaining high computational cost. To address these issues, this paper proposes LiteUAV-Det, a lightweight and real-time UAV-to-UAV detection framework designed to achieve an optimal balance between detection accuracy and computational efficiency. The proposed method introduces a unified lightweight feature refinement strategy based on three key components. First, a Ghost-Enhanced C2f (GEC) module is designed to improve feature representation while reducing redundant computations. Second, a GEC-CSPGhostNet backbone is constructed to enhance multi-scale feature extraction with reduced parameter complexity. Third, a GEC-PANet neck is developed to improve feature fusion using dynamic upsampling and channel-guided spatial attention, enabling better preservation of small-target information. In addition, SPP-LSKA is incorporated to capture long-range spatial dependencies and enhance contextual feature modeling for improved representation of small UAV targets. Extensive experiments on the Det-Fly and DUT-Anti-UAV datasets demonstrate that LiteUAV-Det achieves competitive detection performance compared with state-of-the-art lightweight detectors. The proposed model achieves 94.7% $mAP_{50}$ with only 6.03M parameters and 12.1G FLOPs, while achieving an inference latency of 6.5 ms (154 FPS) on a GPU node under end-to-end evaluation. The results show that LiteUAV-Det provides an effective solution for real-time UAV-to-UAV small object detection in complex aerial environments.
A. Khan, Muhammad Ali Farooq, Xiao-Feng Bai et al.· IEEE Access· 0 citations
A gated feature fusion net is designed to reduce model parameters and a small object detection pyramid is implemented to preserve high-resolution details and integrate a mini-kernel that utilizes a lossless down-sampling module for deep feature optimization.
Jun-Qiu Wang, Xiang-Yang Lu, Yanda Wang et al.· 0 citations
Fixed-wing unmanned aerial vehicles (UAVs) have been widely employed in remote sensing inspection, disaster assessment, traffic surveillance, and ground target recognition because of their long endurance, wide-area coverage, and high imaging efficiency. However, high-resolution imagery captured under large oblique viewing angles is subject to severe perspective distortion and spatially varying ground sampling distance (GSD), resulting in substantial intra-class variations in object scale, shape, and texture. These factors significantly degrade detection performance, particularly for small vehicle-like targets in cluttered backgrounds. Moreover, directly resizing large-format images to the fixed input resolution required by common detection networks inevitably compresses small objects and causes the loss of discriminative details. To address these issues, this paper proposes a scale-normalized object detection method for largeoblique- angle fixed-wing UAV imagery based on an enhanced YOLO11 framework. First, geometric rectification is introduced to alleviate perspective-induced distortions and reduce the spatial inconsistency of object appearance. Second, a fixed-GSD-based local tiling and scale normalization strategy is developed to transform large high-resolution images into uniformly scaled sub-images suitable for network input. The influence of different GSD settings on detection accuracy and inference efficiency is further investigated to determine an appropriate operating scale. Third, an improved YOLO11s detector is constructed by incorporating lightweight re-parameterized convolution, attention-guided feature enhancement, and detection head adaptation, thereby strengthening the representation capability for small targets under complex background conditions. Experimental results demonstrate that the proposed method effectively mitigates the scale inconsistency inherent in large-oblique-angle imagery and improves detection accuracy while maintaining favorable computational efficiency. Compared with the baseline model, the proposed approach achieves consistent gains in mAP@0.5, recall, and small-object detection performance, confirming its effectiveness for fixed-wing UAV large-obliqueangle target detection tasks.
Yu-Qing Wang, Xiaolong Zheng, Yue-Qi Wang et al.· International Conference on...· 0 citations
HD-YOLO improves small-object detection with a compact parameter footprint, while direct hardware benchmarks remain necessary to establish deployment efficiency.
Maosheng Sun, Jing Ding, Yang Zhang et al.· Applied Sciences· 0 citations
Drones serve as critical tools for urban security surveillance and emergency response. However, aerial object detection faces inherent challenges, as key targets such as pedestrians and vehicles often appear tiny, densely distributed, and heavily occluded. These issues reduce detection accuracy and hinder real-world security deployment. To tackle these challenges, this paper proposes WTM-YOLOv11, an improved detection model for aerial surveillance scenarios. The core improvements are threefold: 1) the WIoUv3 loss function is adopted to optimize bounding box regression and enhance the model’s adaptability to complex sample distributions; 2) we integrate a C3k2-WTConv unit (by embedding WTConv into C3k2) into the backbone to enlarge the receptive field and strengthen multi-scale feature extraction; 3) the neck network is optimized to enhance cross-scale feature fusion and small-object representation capability. Comprehensive experiments demonstrate that WTM-YOLOv11 outperforms the vanilla YOLOv11 baseline, achieving a 2.3% improvement in mean average precision (mAP) while retaining a lightweight parameter footprint. The proposed method effectively enhances the detection performance of small and occluded targets in drone aerial imagery, providing a robust and feasible solution for intelligent public security surveillance systems.
