3 September 2024 Vis-YOLO: a lightweight and efficient image detector for unmanned aerial vehicle small objects
Xiangyu Deng, Jiangyong Du
Author Affiliations +
Abstract

Yolo series models are extensive within the domain of object detection. Aiming at the challenge of small object detection, we analyze the limitations of existing detection models and propose a Vis-YOLO object detection algorithm based on YOLOv8s. First, the down-sampling times are reduced to retain more features, and the detection head is replaced to adapt to the small object. Then, deformable convolutional networks are used to improve the C2f module, improving its feature extraction ability. Finally, the separation and enhancement attention module is introduced to the model to give more weight to the useful information. Experiments show that the improved Vis-YOLO model outperforms the YOLOv8s model on the visdrone-2019 dataset. The precision improved by 5.4%, the recall by 6.3%, and the mAP50 by 6.8%. Moreover, Vis-YOLO models are smaller and suitable for mobile deployment. This research provides a new method and idea for small object detection, which has excellent potential application value.

© 2024 SPIE and IS&T
Xiangyu Deng and Jiangyong Du "Vis-YOLO: a lightweight and efficient image detector for unmanned aerial vehicle small objects," Journal of Electronic Imaging 33(5), 053003 (3 September 2024). https://doi.org/10.1117/1.JEI.33.5.053003
Received: 8 May 2024; Accepted: 13 August 2024; Published: 3 September 2024
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KEYWORDS
Object detection

Data modeling

Head

Convolution

Image sensors

Performance modeling

Feature extraction

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