Abstract
In the field of unmanned aerial vehicle (UAV) target detection, the significant vertical fluctuations of UAVs pose a considerable challenge to image detection, particularly in detecting small targets, due to the large variation in the size of the main subjects. To overcome this challenge, we propose a novel algorithm architecture based on YOLO-v8, named MSD-YOLO. Firstly, we design a more innovative network for feature extraction and integration (MUBIFPN) to replace the original Neck part, enabling the model to better fuse features. Secondly, we also design a Feature Pyramid Pooling structure (SPPFCSPC-SM) to replace the original SPPF, enhancing the receptive field of this part. Finally, we introduce an advanced multi-dimensional perception detection head (DyHead) as the detection head of this network, significantly enhancing the expression ability of the detection head. Experiments show that the proposed method achieves a 4.7% improvement in recall rate and a 5.8% improvement in mAP50 on the VisDrone2019 dataset compared to the original YOLO-v8n model. The mAP50-90 is improved by 4.0%. Compared to the larger YOLO-v8s, not only is there a slight improvement in recall rate and accuracy, but also the parameters is reduced by 58.6%, and GFLOPs are reduced by 54.7%.
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Acknowledgement
This research was partially supported by the Inner Mongolia Autonomous RegioNatural Science Foundation Key Project (No. 2024ZD27) and the Inner Mongcous Region Social Science Foundation Comnissioned Key Projeclia Autono! (No. 2024WTZD02).
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Liu, D., Zhu, Y., Liu, R., Xing, Z., Geng, W., Wang, Y. (2025). MSD-YOLO: An Efficient Algorithm for Small Target Detection. In: Ide, I., et al. MultiMedia Modeling. MMM 2025. Lecture Notes in Computer Science, vol 15522. Springer, Singapore. https://doi.org/10.1007/978-981-96-2064-7_5
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DOI: https://doi.org/10.1007/978-981-96-2064-7_5
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