{"title":"LUD-YOLO:用于无人飞行器的新型轻量级物体探测网络","authors":"","doi":"10.1016/j.ins.2024.121366","DOIUrl":null,"url":null,"abstract":"<div><p>Autonomous execution of tasks by unmanned aerial vehicles (UAVs) relies heavily on object detection. However, object detection in most images presents challenges such as complex backgrounds, small targets, and obstructions. Additionally, the limited computing speed and memory of the UAV processor affects the accuracy of conventional object detection algorithms. This paper proposes LUD-You Only Look Once (YOLO), a small and lightweight object detection algorithm for UAVs based on YOLOv8. The proposed algorithm introduces a new multiscale feature fusion mode that solves the degradation in feature propagation and interaction through the introduction of upsampling in the feature pyramid network and the progressive feature pyramid network. The application of the dynamic sparse attention mechanism in the Cf2 module achieves flexible computing allocation and content awareness. Furthermore, the proposed model is optimized to be sparse and lightweight, making it possible to deploy on UAV edge devices. Finally, the effectiveness and superiority of LUD-YOLO were verified on the VisDrone2019 and UAVDT datasets. The results of ablation and comparison experiments show that compared with the original algorithm, LUDY-N and LUDY-S have shown excellent performance in various evaluation indexes, indicating that the proposed improvement strategies make the model have better robustness and generalization. Moreover, compared with multiple other popular competitors, the proposed improvement strategies enable LUD-YOLO to have the best overall performance, providing an effective solution for UAVs object detection while balancing model size and detection accuracy.</p></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":null,"pages":null},"PeriodicalIF":8.1000,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0020025524012805/pdfft?md5=d596a6997eac3036bbb68e709c00a107&pid=1-s2.0-S0020025524012805-main.pdf","citationCount":"0","resultStr":"{\"title\":\"LUD-YOLO: A novel lightweight object detection network for unmanned aerial vehicle\",\"authors\":\"\",\"doi\":\"10.1016/j.ins.2024.121366\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Autonomous execution of tasks by unmanned aerial vehicles (UAVs) relies heavily on object detection. 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Finally, the effectiveness and superiority of LUD-YOLO were verified on the VisDrone2019 and UAVDT datasets. The results of ablation and comparison experiments show that compared with the original algorithm, LUDY-N and LUDY-S have shown excellent performance in various evaluation indexes, indicating that the proposed improvement strategies make the model have better robustness and generalization. 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引用次数: 0
摘要
无人飞行器(UAV)自主执行任务在很大程度上依赖于目标检测。然而,大多数图像中的物体检测都面临着复杂背景、小目标和障碍物等挑战。此外,无人飞行器处理器有限的计算速度和内存也影响了传统物体检测算法的准确性。本文提出了一种基于 YOLOv8 的小型轻量级无人机目标检测算法 LUD--You Only Look Once (YOLO)。该算法引入了新的多尺度特征融合模式,通过在特征金字塔网络和渐进式特征金字塔网络中引入上采样,解决了特征传播和交互中的退化问题。Cf2 模块中动态稀疏关注机制的应用实现了灵活的计算分配和内容感知。此外,所提出的模型经过优化,具有稀疏性和轻量级的特点,可以部署在无人机边缘设备上。最后,在 VisDrone2019 和 UAVDT 数据集上验证了 LUD-YOLO 的有效性和优越性。消融和对比实验结果表明,与原始算法相比,LUDY-N 和 LUDY-S 在各项评价指标上均表现优异,表明所提出的改进策略使模型具有更好的鲁棒性和泛化能力。此外,与其他多个流行的竞争对手相比,所提出的改进策略使 LUD-YOLO 的整体性能最佳,为无人机物体检测提供了有效的解决方案,同时兼顾了模型大小和检测精度。
LUD-YOLO: A novel lightweight object detection network for unmanned aerial vehicle
Autonomous execution of tasks by unmanned aerial vehicles (UAVs) relies heavily on object detection. However, object detection in most images presents challenges such as complex backgrounds, small targets, and obstructions. Additionally, the limited computing speed and memory of the UAV processor affects the accuracy of conventional object detection algorithms. This paper proposes LUD-You Only Look Once (YOLO), a small and lightweight object detection algorithm for UAVs based on YOLOv8. The proposed algorithm introduces a new multiscale feature fusion mode that solves the degradation in feature propagation and interaction through the introduction of upsampling in the feature pyramid network and the progressive feature pyramid network. The application of the dynamic sparse attention mechanism in the Cf2 module achieves flexible computing allocation and content awareness. Furthermore, the proposed model is optimized to be sparse and lightweight, making it possible to deploy on UAV edge devices. Finally, the effectiveness and superiority of LUD-YOLO were verified on the VisDrone2019 and UAVDT datasets. The results of ablation and comparison experiments show that compared with the original algorithm, LUDY-N and LUDY-S have shown excellent performance in various evaluation indexes, indicating that the proposed improvement strategies make the model have better robustness and generalization. Moreover, compared with multiple other popular competitors, the proposed improvement strategies enable LUD-YOLO to have the best overall performance, providing an effective solution for UAVs object detection while balancing model size and detection accuracy.
期刊介绍:
Informatics and Computer Science Intelligent Systems Applications is an esteemed international journal that focuses on publishing original and creative research findings in the field of information sciences. We also feature a limited number of timely tutorial and surveying contributions.
Our journal aims to cater to a diverse audience, including researchers, developers, managers, strategic planners, graduate students, and anyone interested in staying up-to-date with cutting-edge research in information science, knowledge engineering, and intelligent systems. While readers are expected to share a common interest in information science, they come from varying backgrounds such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioral sciences, and biochemistry.