Real-time object detection is critical for unmanned aerial vehicles (UAVs) performing various tasks. However, efficiently deploying detection models on UAV platforms with limited storage and computational resources remains a significant challenge. To address this issue, we propose ELNet, an efficient and lightweight object detection model based on YOLOv12n. First, based on an analysis of UAV image characteristics, we strategically remove two A2C2f modules from YOLOv12n and adjust the size and number of detection heads. Second, we propose a novel lightweight detection head, EPGHead, to alleviate the computational burden introduced by adding the large-scale detection head. In addition, since YOLOv12n employs standard convolution for downsampling, which is inefficient for extracting UAV image features, we design a novel downsampling module, EDown, to further reduce model size and enable more efficient feature extraction. Finally, to improve detection in UAV imagery with dense, small, and scale-varying objects, we propose DIMB-C3k2, an enhanced module built upon C3k2, which boosts feature extraction under complex conditions. Compared with YOLOv12n, ELNet achieves an 88.5% reduction in parameter count and a 52.3% decrease in FLOPs, while increasing mAP₅₀ by 1.2% on the VisDrone dataset and 0.8% on the HIT-UAV dataset, reaching 94.7% mAP₅₀ on HIT-UAV. Furthermore, the model achieves a frame rate of 682 FPS, highlighting its superior computational efficiency without sacrificing detection accuracy.

In remote sensing imagery, detecting small objects is challenging due to the limited representational ability of feature maps when resolution changes. This issue is mainly reflected in two aspects: (1) upsampling causes feature shifts, making feature fusion difficult to align; (2) downsampling leads to the loss of details. Although recent advances in object detection have been remarkable, small-object detection remains unresolved. In this paper, we propose Dual Feature-Aware Sampling YOLO (DFAS-YOLO) to address these issues. First, the Soft-Aware Adaptive Fusion (SAAF) module corrects upsampling by applying adaptive weighting and spatial attention, thereby reducing errors caused by feature shifts. Second, the Global Dense Local Aggregation (GDLA) module employs parallel convolution, max pooling, and average pooling with channel aggregation, combining their strengths to preserve details after downsampling. Furthermore, the detection head is redesigned based on object characteristics in remote sensing imagery. Extensive experiments on the VisDrone2019 and HIT-UAV datasets demonstrate that DFAS-YOLO achieves competitive detection accuracy compared with recent models.

Multiple object tracking (MOT) in videos captured by unmanned aerial vehicle (UAV) is a fundamental aspect of computer vision. Recently, the one-shot tracking paradigm integrates the detection and re-identification (ReID) tasks, striking a balance between tracking accuracy and inference speed. This paradigm alleviates task conflicts and achieves remarkable results through various feature decoupling methods. However, in challenging scenarios like drone movements, lighting changes and object occlusion, it still encounters issues with detection failures and identity switches. In addition, traditional feature decoupling methods directly employ channel-based attention to decompose the detection and ReID branches, without a meticulous consideration of the specific requirements of each branch. To address the above problems, we introduce an asymmetric feature enhancement network with a global coordinate-aware enhancement (GCAE) module and an embedding feature aggregation (EFA) module, aiming to optimize the two branches independently. On the one hand, we develop the GCAE module for the detection branch, which effectively merges rich semantic information within the feature space to improve detection accuracy. On the other hand, we introduce the EFA module for the ReID branch, which highlights the significance of pixel-level features and acquires discriminative identity embedding through a local feature aggregation strategy. By efficiently incorporating the GCAE and EFA modules into the one-shot tracking pipeline, we present a novel MOT framework, named AsyUAV. Extensive experiments have demonstrated the effectiveness of our proposed AsyUAV. In particular, it achieves a MOTA of 38.3% and IDF1 of 51.7% on VisDrone2019, and a MOTA of 48.0% and IDF1 of 67.5% on UAVDT, outperforming existing state-of-the-art trackers.

The one-shot multiple object tracking framework simultaneously generates detected targets and re-identification (ReID) embeddings, and employs them to associate previous tracks. However, the continuity between the learning and extraction within the ReID task is unconsciously neglected because of treating them as two isolated stages. This leads to unreliable ReID embeddings and poses a challenge to target matching especially in complex and crowded scenarios. To this end, we propose a guided embedding enhancement multiple object tracker, named G2EMOT. It consists of three innovative designs aiming at improving the embedding representation capability for individual targets. First, based on the original feature map, a global instance-specific context decoupling (GICD) module is devised to facilitate the respective feature learning for detection and ReID branches. Then, a heatmap guided embedding enhancement (HGEE) module is introduced to connect the processes of embedding learning and extraction, ensuring that the detected target coordinates are accurately aligned with the discriminative ReID embeddings. Finally, to associate and stabilize matched targets, we present a novel two-gate guided embedding update (TG-GEU) strategy that dynamically updates ReID embeddings. With the proposed three components, G2EMOT achieves outstanding performance on popular MOT benchmarks, while outperforming the existing one-shot tracking methods by a large margin. In particular, it realizes IDF1 of 76.1% on MOT17 and IDF1 of 74.2% on MOT20. The source codes are released at https://github.com/ydhcg-BoBo/G2EMOT.

RGB-Thermal multi-object tracking (RGB-T MOT) focuses on tracking multiple objects in complex scenarios, such as nighttime and low-light conditions, which is crucial for various applications, including video surveillance and unmanned aerial vehicle monitoring. Existing RGB-T MOT studies typically merge multi-source features in a single stage before the backbone network. These works fail to preserve fine-grained details and struggle with context-dependent fusion, resulting in suboptimal tracking performance. In this paper, we propose a novel multi-stage cross-modality spatial-temporal feature interaction network (MCTrack), which emphasizes two main aspects: temporal-aware learning and cross-modality information interaction. For temporal-aware learning, we introduce the temporal salient feature interaction (TSFI) module, which ensures trajectory continuity by capturing dynamic spatial variation of objects across RGB-T video pairs, enabling consistent object tracking over time. For cross-modality information interaction, we propose the bidirectional modality interaction (Bi-MI) module, which employs cross-modality transformers to extract complementary features from both RGB and thermal modalities at multiple stages of feature extraction, thereby improving tracking adaptability in diverse tracking scenarios. Additionally, we propose the cross-modality complementary mask (CCM) strategy, which applies non-overlapping random masks to feature maps to improve the robustness of cross-modality feature interaction. Extensive experiments and comparative analyses demonstrate that our MCTrack surpasses state-of-the-art trackers on both publicly available VT-MOT and UniRTL-MOT RGB-T datasets. The code is available at https://github.com/ydhcg-BoBo/RGB-T-MOT.

RGB-Thermal (RGB-T) object detection in Unmanned Aerial Vehicle (UAV) imagery typically relies on fusing multimodal information to detect multiple objects under all-weather conditions. However, existing fusion methods primarily rely on pairwise attention mechanisms, thereby overlooking group-wise dependencies. Such oversight renders these methods ineffective at capturing collective relationships among objects in complex aerial scenes, including vehicle queues or cases of heavy occlusion. Additionally, the densely connected structures in these fusion methods often result in high computational costs. To address these limitations, we propose a lightweight cross-modal hypergraph fusion for RGBT object detection, termed HyperDet. The core of HyperDet is an Adaptive Hierarchical Multi-Modal Fusion (AHMF) framework, which follows a cascaded enhancement-reasoning paradigm. Specifically, AHMF first employs a Dual Cross-Modal Feature Alignment (DCFA) module to decouple and align dense RGB textures and sparse thermal signatures, producing a calibrated feature space. On top of this aligned representation, a Cross-Modal Adaptive Hypergraph (CAH) module is introduced to perform high-order reasoning. Unlike pairwise approaches, CAH constructs dynamic hyperedges that aggregate spatially dispersed but semantically consistent instances across modalities. This mechanism enables the model to explicitly capture long-range, group-wise dependencies among objects, allowing for robust recognition of complex clusters. Extensive experiments on three RGBT object detection benchmarks, i.e., DroneVehicle, VEDAI, and LLVIP, demonstrate that HyperDet achieves state-of-the-art performance. For example, it achieves 86.4% mAP\(_{50}\) on DroneVehicle with only 3.2 M parameters. The code for HyperDet is available at https://github.com/lihui-ioe/HyperDet.