Jul 2026· 2026 IEEE 27th China Conference on System Simulation Technology and its Applications (CCSSTA)· pp. 1197-1203· 0 citations· 36 references
Abstract
Small-object detection in unmanned aerial vehicle (UAV) imagery is challenging because targets are often tiny, densely distributed, and embedded in cluttered backgrounds, while edge platforms impose strict computational constraints. This paper presents a lightweight end-to-end detector based on RT-DETR to improve detection accuracy, localisation quality, and deployment efficiency. The proposed method includes three components: a detail-enhanced backbone for preserving fine-grained texture and edge cues, rotary positional encoding combined with a content-aware bidirectional feature pyramid for improved cross-scale alignment, and a difficulty-aware composite regression loss for more stable bounding-box optimisation. Experiments on VisDrone2019 and DOTA show that the proposed method improves mAP50 by 3.5% and 1.6%, respectively, over the RT-DETR baseline, while reducing the number of parameters by 28.6%. Ablation studies confirm the complementary contributions of the three components, and robustness experiments demonstrate reliable performance under challenging aerial conditions. Deployment on a Jetson Orin NX using a self-built UAV dataset further validates the practicality of the method for edge-side UAV monitoring.
Small-object detection in unmanned aerial vehicle (UAV) imagery is hindered by limited target pixels, dense spatial distributions, background interference, and scale-sensitive bounding-box regression. This study develops a lightweight YOLO11n configuration in which established SCSA recalibration, DySample reconstruction, decoupled prediction, and SimOTA assignment act at successive stages of the detection pipeline. Its principal methodological contribution is a gradient-adaptive WIoU–NWD objective that uses previously observed regression-gradient fluctuations to balance overlap-oriented and distribution-based localization without altering the inference graph. On the official VisDrone2019-DET test-dev server, the 2.48 M-parameter model achieves 45.9% mAP@0.5, 27.0% mAP@0.5:0.95, and 20.2% APsmall, improving the YOLO11n baseline by 2.3, 1.4, and 2.4 percentage points, respectively; it also improves mAP@0.5/mAP@0.5:0.95 by 2.2/1.3 points on the vehicle-focused UAVDT benchmark and reaches 40.2 FPS on a Jetson Orin NX in 15 W mode using TensorRT FP16. The two benchmarks mainly represent urban, traffic, and low-altitude surveillance imagery; consequently, the cross-dataset result supports transfer within these conditions rather than universal generalization to all UAV applications. These results support a compact single-pass accuracy–efficiency trade-off for resource-constrained UAV perception, while the modest margin over fixed loss weighting indicates that the adaptive mechanism should be interpreted as an incremental, primarily small-object localization improvement rather than a complete solution to regression instability.
Small object detection in Unmanned Aerial Vehicle (UAV) imagery remains challenging under adverse conditions, including complex weather, low illumination, and sensor noise. These challenges mainly stem from severe background clutter, fine-grained detail degradation, and suboptimal semantic-spatial feature fusion, which jointly hinder robust small-object representation. To this end, we propose FRFDet, a lightweight yet effective single-stage detector tailored for UAV-based small object detection. FRFDet proposes two plug-and-play modules: Inverse Bidirectional Sampling (IBS) and Scale-Feature Relationship Cross-Fusion (SFRCF). IBS preserves critical spatial details via channel expansion-compression and bidirectional pattern reconstruction, improving feature alignment. SFRCF explicitly models scale-dependent fusion behaviors, revealing that inter-group element-wise multiplication favors compact models, while inter-group additive fusion benefits larger architectures. Extensive experiments on VisDrone, UAVDT, HazyDet, and MS COCO demonstrate that FRFDet achieves state-of-the-art performance among lightweight detectors with low computational cost, compact parameters, and fast inference, making it well suited for resource-constrained UAV platforms.
Yunzhong Si, Huiying Xu, Xinzhong Zhu et al.· 1 citation
Compared to existing state-of-the-art methods, LightS-DETR delivers superior detection accuracy and robust real-time performance while significantly reducing computational demands, offering an optimal balance for UAV deployment.
Xi Cai, Shasha Zhao, Changsuo Yu et al.· Journal of Real-Time Image P...· 0 citations
Small object detection in unmanned aerial vehicle (UAV) images is challenging due to small object scale, dense distribution, and complex background interference. These factors make it difficult to balance detection accuracy and computational efficiency. To address this issue, this study proposes a lightweight real-time object detection framework, termed EAMS-YOLO. The method reconstructs the detection scale by introducing a high-resolution P 2 feature layer and removing the P 5 feature layer. This design enhances fine-grained feature representation for small objects. Meanwhile, a multi-scale edge information enhancement module and a multi-scale linear attention module are designed to strengthen fine-grained feature representation and cross-scale contextual modeling. In addition, a Shape-IoU-based shape-aware loss is introduced to improve object localization accuracy. Experiments on the VisDrone2019 dataset show that, compared with the YOLOv11 baseline, EAMS-YOLO achieves gains of 6.2% and 3.4% in mAP50 and mAP50:95, respectively. Meanwhile, the number of model parameters is reduced to 0.9 M. Experimental results on the DOTA and PASCAL VOC datasets indicate that the proposed method provides stable performance gains across different scenarios and object scales. Furthermore, in engineering degradation tests and edge device evaluations, the model maintains robust detection performance under challenging imaging conditions and resource-limited environments. The proposed EAMS-YOLO provides a balanced trade-off among detection accuracy, model complexity, and deployment adaptability, making it well suited for UAV-based small object detection in resource-constrained environments.
Ruilin Pan, Xialian Sang, Kai Wang et al.· Engineering Research Express· 0 citations
Unmanned aerial vehicle (UAV) imagery supports intelligent surveillance, environmental monitoring, traffic management, and infrastructure inspection. Yet aerial detection is difficult when objects are small, crowded, and observed at markedly different scales. Background clutter and illumination changes further weaken target cues and impair localization. We therefore propose HD-YOLO, a lightweight multiscale detector for small objects in UAV imagery. Its Multi-Dilation Shared Convolution Kernel (DSCK) extracts local texture and contextual information with shared dilated kernels. The Hybrid Dilated Bidirectional Feature Pyramid Network (HDFPN) reconstructs global and local cues before bidirectional aggregation, enabling high-resolution evidence to reach the prediction layers. The Efficient and Slim Head (ES-Head) combines shared operations with differential convolution to reduce cost and strengthen boundary-sensitive features. A joint ShapeIoU and Normalized Wasserstein Distance loss improves regression for small, irregular objects. Together, these components reduce missed detections in dense, cluttered scenes without relying on large model capacity. On VisDrone2019, HD-YOLO improves precision, recall, mAP50, and mAP50:95 over YOLOv8n by 6.9%, 7.2%, 8.2%, and 5.2%, respectively, while reducing parameters from 3.0 M to 0.9 M. Evaluations on TinyPerson and HIT-UAV also support its utility for tiny pedestrians and infrared aerial targets. HD-YOLO therefore 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
Detecting small unmanned aerial vehicles (UAVs) in ground-to-air imagery is challenging because their weak visual cues must be preserved without imposing excessive computation on resource-constrained platforms. To address the unresolved trade-off between tiny-target representation and deployment efficiency, we propose a Lightweight Feature-Fusion and Small-Target Enhancement Network (LFE-YOLO), a lightweight detector that coordinates partial-channel feature extraction, efficient cross-scale fusion, high-resolution prediction, background-interference suppression, and stable tiny-box regression within a unified architecture. Specifically, C2fFaster and GSConv reduce redundant computation while maintaining multi-scale feature propagation; a P2 high-resolution detection branch and Efficient Multi-scale Attention preserve fine spatial cues and suppress background interference; and Normalized Wasserstein Distance complements Complete Intersection over Union to improve tiny-box localization stability. We also construct Det-UAV by integrating newly collected multi-platform and multi-scene UAV imagery with existing data using scene- and sequence-independent partitioning and duplicate control. Experiments on Det-UAV show that LFE-YOLO improves detection accuracy while reducing parameters and computation relative to YOLOv8s. Zero-shot evaluation on the public DUT Anti-UAV dataset further indicates favorable transferability to an unseen data distribution. TensorRT 8.2.1 deployment experiments on NVIDIA Jetson TX2 show that, under the same evaluation settings, LFE-YOLO achieves higher detection accuracy and inference throughput, lower latency, and a smaller engine size than the comparable-scale YOLOv8s and YOLO11s models. These results support a practical accuracy–efficiency balance for small-UAV detection under constrained resources.
Mingxi Chen, Cheng Guo, S. Ma et al.· Drones· 0 citations