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YOLO-TUF: An Improved YOLOv5 Model for Small Object Detection

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Artificial Intelligence and Machine Learning (IAIC 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2058))

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Abstract

Traditional target detection algorithms frequently encounter challenges in accurately detecting objects within complex, cluttered environments. This paper presents an optimized YOLOv5-based model to mitigate such limitations. Our contributions are threefold: Firstly, we enhance the upsampling procedure by amalgamating transposed convolution with the CBAM attention mechanism, fortifying the network’s fine-grained feature extraction capabilities. Secondly, we introduce an optimized feature-processing module, which enhances feature utilization while maintaining a lightweight architecture. Lastly, we integrate EfficientNet into the backbone architecture to amplify feature extraction performance. We validate our approach using the PASCAL VOC dataset, achieving an mAP0.5 of 84.00% and an mAP0.5:0.95 of 62.10%, while maintaining a modest parameter size of 13.22MB. These results mark an improvement of 4.50% ± 0.12% and 8.20% ± 0.09% over the benchmark, demonstrating an efficient trade-off between computational efficiency and detection accuracy. The proposed model outperforms conventional YOLOv5 algorithms and remains competitive with contemporary state-of-the-art object detection techniques. Code is available at https://github.com/chenxz0906chenxz/YOLO-TUF/.

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Authors and Affiliations

  1. School of Computer and Software Engineering, Xihua university, Chengdu, 610039, People’s Republic of China

    Hua Chen, Wenqian Yang, Wei Wang & Zhicai Liu

Authors
  1. Hua Chen
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  2. Wenqian Yang
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  3. Wei Wang
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  4. Zhicai Liu
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Corresponding author

Correspondence to Zhicai Liu .

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Editors and Affiliations

  1. Huazhong University of Science and Technology, Wuhan, Hubei, China

    Hai Jin

  2. Chinese Academy of Science, Shenzhen, China

    Yi Pan

  3. Nanjing University of Science and Technology, Nanjing, China

    Jianfeng Lu

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Chen, H., Yang, W., Wang, W., Liu, Z. (2024). YOLO-TUF: An Improved YOLOv5 Model for Small Object Detection. In: Jin, H., Pan, Y., Lu, J. (eds) Artificial Intelligence and Machine Learning. IAIC 2023. Communications in Computer and Information Science, vol 2058. Springer, Singapore. https://doi.org/10.1007/978-981-97-1277-9_37

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  • DOI: https://doi.org/10.1007/978-981-97-1277-9_37

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-1276-2

  • Online ISBN: 978-981-97-1277-9

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