LUD-YOLO: A novel lightweight object detection network for unmanned aerial vehicle

Fan, Qingsong; Li, Yiting; Deveci, Muhammet; Zhong, Kaiyang; Kadry, Seifedine

doi:10.1016/j.ins.2024.121366

Information Sciences

2025

DOI: 10.1016/j.ins.2024.121366

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LUD-YOLO: A novel lightweight object detection network for unmanned aerial vehicle

Qingsong Fan,

Yiting Li,

Muhammet Deveci

et al.

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2025

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Cited by 6 publications

References 29 publications

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LightUAV-YOLO: a lightweight object detection model for unmanned aerial vehicle image

Lyu,

Zhang,

et al. 2024

J Supercomput

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LightUAV-YOLO: a lightweight object detection model for unmanned aerial vehicle image

Lyu,

Zhang,

et al. 2024

J Supercomput

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An Evaluation of Image Slicing and YOLO Architectures for Object Detection in UAV Images

Telçeken,

Akgun,

Kacar

2024

Applied Sciences

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Object detection in aerial images poses significant challenges due to the high dimensions of the images, requiring efficient handling and resizing to fit object detection models. The image-slicing approach for object detection in aerial images can increase detection accuracy by eliminating pixel loss in high-resolution image data. However, determining the proper dimensions to slice is essential for the integrity of the objects and their learning by the model. This study presents an evaluation of the image-slicing approach for alternative sizes of images to optimize efficiency. For this purpose, a dataset of high-resolution images collected with Unmanned Aerial Vehicles (UAV) has been used. The experiments evaluated using alternative YOLO architectures like YOLOv7, YOLOv8, and YOLOv9 show that the image dimensions significantly change the performance results. According to the experiments, the best mAP@05 accuracy was obtained by slicing 1280×1280 for YOLOv7 producing 88.2. Results show that edge-related objects are better preserved as the overlap and slicing sizes increase, resulting in improved model performance.

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EDT-YOLOv8n-Based Lightweight Detection of Kiwifruit in Complex Environments

Chen,

Hu,

Cheng

et al. 2025

Electronics

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Automated kiwi harvesting hinges on the seamless deployment of a detection model and the accurate detection of kiwifruits. However, practical challenges, such as the limited computational resources on harvesting robots and occlusions among fruits, hinder the effectiveness of automated picking. To address these issues, this paper introduces EDT-YOLOv8n, a lightweight and efficient network architecture based on YOLOv8n. The proposed model integrates the Effective Mobile Inverted Bottleneck Convolution (EMBC) module to replace the C2f modules, mitigating the channel information loss and bolstering generalization. Additionally, the DySample upsampler, an ultra-lightweight and effective dynamic upsampler, improves feature extraction and resource efficiency when compared to traditional nearest-neighbor upsampling. Furthermore, a novel Task Align Dynamic Detection Head (TADDH) is implemented, incorporating group normalization for a more efficient convolutional structure and optimizing the alignment between the classification and localization tasks. The experimental results reveal that the proposed EDT-YOLOv8n model achieves higher precision (86.1%), mAP0.5 (91.5%), and mAP0.5-0.95 (65.9%), while reducing the number of parameters, the number of floating-point operations, and the model size by 15.5%, 12.4%, and 15.0%, respectively. These improvements demonstrate the model’s effectiveness and efficiency in supporting kiwifruit localization and automated harvesting tasks.

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LUD-YOLO: A novel lightweight object detection network for unmanned aerial vehicle

Cited by 6 publications

References 29 publications

LightUAV-YOLO: a lightweight object detection model for unmanned aerial vehicle image

LightUAV-YOLO: a lightweight object detection model for unmanned aerial vehicle image

An Evaluation of Image Slicing and YOLO Architectures for Object Detection in UAV Images

EDT-YOLOv8n-Based Lightweight Detection of Kiwifruit in Complex Environments

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