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.
To address the challenges of UAV aerial imagery, including the prevalence of small objects, complex background interference, and difficulty in feature extraction that lead to high missed detection rates and compromise detection accuracy in existing RT-DETR algorithms, this paper proposes an improved small-object-oriented detector named MSFE-DETR (Multi-Scale Feature Enhancement DETR). A CMFE (CSP-MultiScale Feature Enhancement) module is integrated into the shallow backbone layers to enhance feature representation of small objects and alleviate feature loss caused by scale and background complexity. In deeper layers of backbone, the C2f module is employed to preserve fine-grained details and improve target–background discrimination, while multi-scale feature fusion further prevents small object information degradation. In addition, Deformable Attention (DAttention) is incorporated to adaptively focus on small target regions, retaining spatial positional information and suppressing background noise. The head integrates MPCA and FSA modules, where MPCA progressively fuses adjacent-scale features to complementarily enhance small object representations and suppress background interference, and FSA further improves detail enhancement and robustness. Moreover, an Inner-SIoU loss is proposed by combining Inner-IoU with SIoU, improving localization accuracy, convergence speed, and robustness in complex scenes. Experimental results on the VisDrone 2019 dataset show that MSFE-DETR outperforms RT-DETR-r18 by 1.9% in Precision, 2.1% in Recall and 2.4% in mAP@0.5, while real-time inference is maintained at 68.7 FPS. On the infrared HIT-UAV and satellite SIMD datasets, mAP@0.5 improves by 5.8% and 2.3% respectively over the baseline.
Improved PF-DETR improves the model’s robustness and accuracy in detecting multi-scale and small targets in complex and cluttered scenes, resulting in a favorable balance between detection performance and model efficiency.
Small-object detection in unmanned aerial vehicle (UAV) imagery remains challenging because target objects often occupy only a few pixels, exhibit weak feature responses, and are easily obscured by complex backgrounds. These aspects significantly limit the effectiveness of end-to-end detection systems. To overcome these limitations and enhance the detection accuracy in challenging UAV settings, this paper proposes MPC-DETR, a Multi-scale Patch Context Transformer that is based on RT-DETR. To begin with, a Local-Global Attention Fusion Module (LGAF) is proposed to capture fine-grained local features and long-range semantic relations of small objects. LGAF enhances feature representation through a lightweight multi-branch synergistic attention mechanism while introducing limited computational overhead. Second, a Dilated Context-Aware Feature Interaction Module (DCFI) is proposed to enhance the discriminative capability of high-level features in cluttered backgrounds and densely populated small-object scenes. DCFI allows more efficient feature aggregation and contextual comprehension through multi-scale contextual modeling and scale-adaptive feature interaction. Third, a Patch-Guided Multi-scale Feature Fusion Module (PGMFF) is developed to create a patch-guided contextual fusion approach that combines shallow, high-resolution features with deeper semantic information. This process improves the maintenance and representation of fine object information and minimizes information loss in feature propagation. The experimental results on the VisDrone2019 dataset show that MPC-DETR has an mAP50 and mAP50–95 of 52.5% and 33.3%, respectively, which are 4.6 and 4.0 percentage points higher than the baseline model. Further analyses of the UAVDT and HIT-UAV datasets also support the high generalization potential of the suggested method to various UAV-based small-object detection problems. In general, the findings suggest that MPC-DETR provides precise, strong, and efficient small-object detection in complicated UAV images.
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.
Junfu Chen, Xi Zhao· International Conference on...· 0 citations
Unmanned aerial vehicle (UAV) imagery is a core data source for remote sensing interpretation, intelligent transportation, urban monitoring, and disaster assessment, yet its large scale variation, dense object distributions, and complex backgrounds continue to challenge automated detection systems. Transformer-based detectors offer strong global modeling capacity, but existing implementations still suffer from insufficient multi-scale feature interaction, weak discriminative representation, and loss of fine-grained spatial detail, which together limit performance on small and densely arranged targets. This paper proposes MSF-DETR, a multi-scale feature enhancement framework built on RT-DETR that integrates four coordinated components: an Enhanced Feature Connection (EFC) module for adaptive cross-scale interaction, a Feature Channel Attention (FCA) module for frequency-domain discriminative enhancement, a Reinforced Attention Feedback Module (RAFM) for spatial-detail preservation within the Transformer encoder, and a Unified Query Supervision Loss (UQSL) for stable dense-scene supervision. On the DIOR benchmark, MSF-DETR achieves 86.3% mAP50 and 64.4% mAP50–95, improving on the RT-DETR baseline by 2.8 and 2.6 percentage points, respectively; on DOTA, it reaches 77.2% mAP50 and 48.8% mAP50–95, improvements of 4.7 and 4.6 points. These results demonstrate that jointly coordinating multi-scale fusion, channel discrimination, spatial-detail retention, and query-level supervision, rather than stacking independent modules, yields measurable robustness gains for small and densely distributed objects in UAV aerial imagery.
Shan Dan, Zan-Qi Qiu, Da-Di Cai et al.· Italian National Conference...· 0 citations
Small-object detection in unmanned aerial vehicle (UAV) remote sensing imagery is challenged by dense target distributions, substantial scale variation, complex ground backgrounds, and limited edge-computing resources. To address these challenges, we propose CDF-DETR, an end-to-end detector derived from the Real-Time Detection Transformer (RT-DETR). First, a Cross-Stage Partial Single-Head Attention Transformer (CSP-SHAT) backbone combines efficient local feature extraction with partial-channel global interaction to improve multi-scale representation while reducing the parameter count of the backbone. Second, a dual-scale feature calibration (DSFC) module sequentially performs contextual aggregation and deformable spatial alignment, thereby improving the consistency of shallow localization features and deep semantic features. Third, Focaler-MPDIoU integrates coordinate-sensitive regression with IoU-quality-based sample reweighting for dense small-object localization. Experiments on the VisDrone-2019 test set and the UAVDT and HIT-UAV validation sets demonstrate mAP50 improvements of 3.1, 1.4, and 3.0 percentage points, respectively, over the RT-DETR-R18 baseline. On the VisDrone-2019 validation set, CDF-DETR improves mAP50–95 from 26.20% to 28.52%, corresponding to a gain of 2.32 percentage points, while reducing the parameter count by 25.7%. A compressed INT8 variant achieves 20.84 FPS for an offline image-level pipeline on an NVIDIA Jetson Orin Nano using ONNX and TensorRT. These results demonstrate improved detection accuracy with a reduced parameter footprint for UAV remote sensing image analysis.
