RSS-YOLO: An efficient UAV object detector with reciprocal spatial-semantic fusion

LFC-YOLO: A Lightweight Feature-Complementary YOLO Framework for Small Object Detection in UAV-Based Visual Sensing

Object detection in unmanned aerial vehicle (UAV)-based visual sensing is important for aerial monitoring and intelligent perception. However, it remains difficult because camera-captured aerial images often contain small targets, cluttered backgrounds, occlusion, and limited edge-computing resources. We propose LFC-YOLO, a lightweight feature-complementary detector for small objects in UAV imagery. The main component of LFC-YOLO is the Tiny Object-Specific Detection Architecture (TSD-Arch), which removes redundant computation from deep layers and builds a shallow high-resolution feature pyramid to preserve localization cues for small targets. To reduce the extra cost introduced by high-resolution feature fusion, lightweight GSConv is integrated into the reconstructed neck. In addition, we embed a Feature Complementary Mapping (FCM) block into the C2f backbone structure and form a C2f-based Feature Complementary Mapping (C2f-FCM) module. This module combines semantic and spatial information and reduces interference from complex backgrounds. Experiments on VisDrone2019 show that LFC-YOLO improves the mean average precision at an intersection-over-union threshold of 0.5 (mAP50) by 4.0 percentage points over YOLOv8s while reducing the number of model parameters by 73.9%. Additional evaluation on UAVDT shows that the proposed design remains effective across different UAV scenarios.

LSO-YOLO: a lightweight real-time object detection network for UAV small-object detection

SPAF: Enhancing YOLO-Based Object Detection Using Sparse Attention and Progressive Adaptive Fusion

Detecting objects in Autonomous Aerial Vehicles (AAVs) imagery remains a challenging task due to large variations in object sizes and complex backgrounds, which often leads to missed detections and reduced accuracy, especially for small objects that contain limited visual information and are easily lost during feature extraction. To address these challenges, this paper proposes SPAF, an enhanced YOLO-based model built upon YOLO26 that improves object detection through the integration of a multi-scale attention mechanism, progressive feature aggregation, and adaptive feature fusion. The proposed architecture enhances the backbone network by integrating a Sparse Vision Attention (SVA) module to facilitate the extraction of fine-grained and task-relevant features in complex aerial scenes. In the neck, the Progressive Bidirectional Auxiliary Pyramid Network (PBAP-Net) facilitates efficient cross-scale feature aggregation by combining top-down semantic information with bottom-up spatial details, thereby strengthening multi-scale representation. Finally, Adaptive Channel Multi-Scale Fusion Head(ACMF-Head) refines the final feature representations by adaptively fusing multi-scale information and reducing inconsistencies across feature levels, resulting in more reliable and robust predictions. Experimental results on the VisDrone2019 dataset demonstrate that SPAF achieves 46.8% mAP@0.5, which is 7.5% higher compared to the baseline model. These results confirm the effectiveness of the proposed approach for robust AAV object detection in challenging aerial environments.

Enhancing UAV aerial small object detection through fine-scale bidirectional multi-scale fusion

LUD-YOLO: An Efficient and Balanced Object Detection Model for Unmanned Aerial Vehicles

Unmanned aerial vehicles (UAVs) have been widely used in defense, precision agriculture, ecological monitoring, and intelligent transportation because of their compact size, high mobility, and flexible deployment. Object detection based on UAV imagery is a key technique for autonomous perception and mission execution. However, UAV images usually contain complex backgrounds, small objects, dense target distributions, occlusions, and large-scale variations, which make it difficult for existing detectors to achieve a good balance between detection accuracy and computational efficiency. To address these challenges, this paper proposes LUD-YOLO (Lightweight UAV Detection–YOLO), an efficient UAV object detection model built on YOLOv7. First, an InceptionNeXt module is introduced into the backbone network to enhance multi-scale feature extraction. Second, an Efficient Multi-Scale Attention (EMA) module is embedded in the feature fusion network to suppress background interference and strengthen discriminative target regions. Third, a P2 detection head is added to improve the sensitivity and localization accuracy of small objects. Experimental results on the VisDrone2019 and UAVDT benchmarks demonstrate the effectiveness of the proposed method. On VisDrone2019, LUD-YOLO-n improves mAP@0.5 from 31.2% to 38.2% and mAP@0.5:0.95 from 18.5% to 22.4% compared with YOLOv7-n. LUD-YOLO-s achieves an mAP@0.5 of 41.7%, outperforming YOLOv7-s by 8.9 percentage points. On UAVDT, LUD-YOLO achieves an mAP@0.5 of 68.2%, providing supplementary evidence of cross-dataset applicability on another UAV detection benchmark. The parameter count of LUD-YOLO-n increases by only 7.7%, from 6.89M to 7.42M, while its mAP@0.5 improves by 22.4% relative to the baseline. These results indicate that LUD-YOLO achieves a favorable balance between accuracy and computational cost, making it suitable for UAV platforms with limited computing resources.

SSM-YOLO11s: a lightweight and efficient model for small object detection in UAV aerial imagery

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.