DCP-YOLO: A Lightweight Anti-UAV Object Detection Network

LIU Jia; MAO Die; JIANG Zirui; CAO Yihan; CHEN Dapeng

doi:10.11999/JEIT261031

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LIU Jia, MAO Die, JIANG Zirui, CAO Yihan, CHEN Dapeng. DCP-YOLO: A Lightweight Anti-UAV Object Detection Network[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT261031

Citation:

LIU Jia, MAO Die, JIANG Zirui, CAO Yihan, CHEN Dapeng. DCP-YOLO: A Lightweight Anti-UAV Object Detection Network[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT261031

LIU Jia, MAO Die, JIANG Zirui, CAO Yihan, CHEN Dapeng. DCP-YOLO: A Lightweight Anti-UAV Object Detection Network[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT261031

Citation:

LIU Jia, MAO Die, JIANG Zirui, CAO Yihan, CHEN Dapeng. DCP-YOLO: A Lightweight Anti-UAV Object Detection Network[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT261031

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DCP-YOLO: A Lightweight Anti-UAV Object Detection Network

doi: 10.11999/JEIT261031 cstr: 32379.14.JEIT261031

LIU Jia^{1, 2, 3, 4
,},
MAO Die^{1, 2},
JIANG Zirui^{1, 2},
CAO Yihan^{1, 2, 3, 4},
CHEN Dapeng^{1, 2, 3, 4
,
,}

1.
School of Automation, Nanjing University of Information Science & Technology, Nanjing 210044, China
2.
Jiangsu Province Engineering Research Center of Intelligent Meteorological Exploration Robot, Nanjing 210044, China
3.
Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing 210044, China
4.
Jiangsu Key Laboratory of Big Data Analysis and Intelligent Systems, Nanjing 210044, China

Funds: National Natural Science Foundation of China (62473200 and 62203226), Jiangsu Province Youth Science and Technology Talent Support Project (JSTJ-2024-195), “Qinglan Project” of Jiangsu Province

Accepted Date: 2026-10-19
Rev Recd Date: 2026-10-19

Available Online: 2026-10-28

Abstract

Abstract

Objective With the rapid development of the low-altitude economy, unmanned aerial vehicles (UAVs) have been widely used in aerial photography, logistics, environmental monitoring, and emergency rescue. However, unauthorized UAV operations pose increasing challenges to public safety and low-altitude airspace management. Vision-based anti-UAV detection has attracted considerable attention due to its relatively low cost, rich visual information, and convenient deployment. Compared with conventional objects, UAV targets in low-altitude surveillance images often exhibit small scales, weak textures, significant pose variations, and complex background interference. During repeated feature downsampling, the edge and structural information of these targets may be weakened or lost, resulting in missed detections, false alarms, and inaccurate localization. Furthermore, practical anti-UAV detection systems must balance detection accuracy, computational complexity, and real-time performance under limited hardware resources. Existing lightweight detection networks may struggle to preserve discriminative spatial details while maintaining computational efficiency, particularly when UAV targets occupy only a small proportion of the input image. To address these challenges, a lightweight anti-UAV object detection network named DCP-YOLO is proposed based on YOLOv26n. The objective is to enhance the feature representation and detection accuracy of tiny UAV targets while maintaining a compact model structure and real-time inference capability. Methods The proposed DCP-YOLO is developed based on the lightweight YOLOv26n framework to improve the detection performance of tiny UAV targets while maintaining real-time inference. The network consists of three collaboratively optimized components. In the backbone network, a lightweight feature extraction module named DPC3k2 is designed by integrating depthwise separable convolution (DSConv) and pinwheel-shaped convolution (PConv) to enhance multi-directional feature representation with reduced computational complexity, enabling more discriminative feature extraction for small UAV targets. DSConv reduces redundant spatial computation through the separation of spatial feature extraction and channel mapping, while PConv employs asymmetric convolutional branches to capture directional edge and contour information. During feature fusion, a learnable reverse upsampling module named CUS replaces the conventional interpolation-based upsampling operation to recover high-frequency edge and texture information, thereby improving feature reconstruction quality. In the neck network, a contextual attention enhancement module named C2PSCAA is constructed by incorporating Context Anchor Attention (CAA) into the C2PSA structure to strengthen long-range spatial dependency modeling and multi-scale contextual representation with only a slight computational overhead. Through the collaborative optimization of lightweight feature extraction, feature reconstruction, and contextual attention modeling, the proposed network effectively enhances the representation capability of tiny UAV targets while maintaining low computational complexity and high inference efficiency. Results and Discussions Extensive experiments are conducted on the DUT Anti-UAV, TIB-Net, and VisDrone2019 datasets. The ablation results in Tables 2 and 4 demonstrate that the proposed DPC3k2, CUS, and C2PSCAA modules contribute to lightweight feature extraction, high-frequency detail reconstruction, and contextual feature modeling, respectively. On the DUT Anti-UAV dataset, their collaborative integration improves Precision, Recall, mAP@50, and mAP@50-95 to 98.7%, 90.0%, 92.4%, and 61.7%, respectively, while reducing the number of parameters to 2.29 M and maintaining an inference speed of 210.71 FPS. Similar performance improvements are observed on the TIB-Net dataset, confirming the effectiveness and complementarity of the three modules under different target scales and background conditions. The visualization results in Fig. 6 further show that the proposed method reduces missed detections and false alarms for tiny UAV targets in cluttered scenes. The comparison results in Table 5 indicate that DCP-YOLO outperforms representative YOLO-series detectors in detection accuracy while maintaining lower model complexity and a smaller model size. Compared with the baseline YOLOv26n, mAP@50 and mAP@50-95 are improved from 90.0% and 59.4% to 92.4% and 61.7%, respectively, whereas the computational complexity is reduced from 5.2 GFLOPs to 5.0 GFLOPs. The convergence curves in Fig. 8 show that DCP-YOLO maintains higher Precision and mAP@50-95 during training. Furthermore, Table 6 demonstrates that the proposed method achieves competitive or superior performance compared with recent anti-UAV and small-object detection methods while preserving a lightweight architecture. The detection results in Fig. 7 visually confirm its stronger ability to identify tiny UAV targets under complex backgrounds. The cross-dataset results in Tables 7 and 8 show that DCP-YOLO also achieves improved detection performance on VisDrone2019 while reducing the number of parameters, computational complexity, and model size. Although VisDrone2019 differs substantially from anti-UAV datasets in target categories and imaging viewpoints, the proposed method still improves mAP@50-95 by 1.1% over YOLOv26n, demonstrating favorable cross-scene adaptability and generalization capability. To investigate practical deployment feasibility, the models are further evaluated on an NVIDIA Jetson Orin Nano platform using TensorRT acceleration under a 25 W power setting, as illustrated in Fig. 9. As shown in Table 9, DCP-YOLO achieves an mAP@50 of 94.59%, an mAP@50-95 of 62.70%, and an inference speed of 116.02 FPS in FP16 mode, with a memory consumption of 3.00 GB. Although its inference speed is lower than that of the baseline on the edge platform, DCP-YOLO maintains real-time inference capability while achieving higher detection accuracy. These findings indicate that reductions in theoretical computational complexity do not necessarily translate into proportional improvements in actual inference speed, particularly when frequency-domain operations are involved. Conclusions A lightweight anti-UAV object detection network named DCP-YOLO is proposed to address the challenges of weak feature representation, high-frequency detail degradation, and limited computational resources in low-altitude UAV detection. By jointly optimizing lightweight feature extraction, learnable upsampling, and contextual attention modeling, the proposed method enhances the detection accuracy of tiny UAV targets while maintaining a compact network structure. Experimental results on DUT Anti-UAV, TIB-Net, and VisDrone2019 demonstrate the effectiveness of the proposed modules and their applicability under different scene conditions. Compared with the baseline network, DCP-YOLO achieves higher detection accuracy with fewer parameters and lower theoretical computational complexity. Edge deployment experiments further demonstrate that the proposed method can maintain real-time inference capability on a resource-constrained platform, despite the additional computational overhead associated with its feature reconstruction operations. Nevertheless, the detection of extremely small targets under severe occlusion, adverse weather, and low-light conditions remains challenging. In addition, the current network primarily relies on single-frame visual information and does not explicitly exploit temporal relationships between consecutive frames. Future work will investigate the integration of target tracking, temporal feature modeling, and multimodal perception, together with further optimization of computationally intensive operations, to improve detection robustness and deployment efficiency in complex low-altitude environments.
- Small object detection,
- Anti-UAV detection,
- YOLOv26n,
- Lightweight object detection

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References(33)

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