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DS MYOLO: A Reliable Object Detector Based on SSMs for Driving Scenarios

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Pattern Recognition (ICPR 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15330))

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Abstract

Accurate real-time object detection enhances the safety of advanced driver-assistance systems, making it an essential component in driving scenarios. With the rapid development of deep learning technology, CNN-based YOLO real-time object detectors have gained significant attention. However, the local focus of CNNs results in performance bottlenecks. To further enhance detector performance, researchers have introduced Transformer-based self-attention mechanisms to leverage global receptive fields, but their quadratic complexity incurs substantial computational costs. Recently, Mamba, with its linear complexity, has made significant progress through global selective scanning. Inspired by Mamba’s outstanding performance, we propose a novel object detector: DS MYOLO. This detector captures global feature information through a simplified selective scanning fusion block (SimVSS Block) and effectively integrates the network’s deep features. Additionally, we introduce an efficient channel attention convolution (ECAConv) that enhances cross-channel feature interaction while maintaining low computational complexity. Extensive experiments on the CCTSDB 2021 and VLD-45 driving scenarios datasets demonstrate that DS MYOLO exhibits significant potential and competitive advantage among similarly scaled YOLO series real-time object detectors.

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Acknowledgements

This work is supported by China NSFC Program under Grant NO. 61603257.

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Authors and Affiliations

  1. University of Shanghai for Science and Technology, Shanghai, 200093, China

    Yang Li & Jianli Xiao

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  1. Yang Li
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  2. Jianli Xiao
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Correspondence to Jianli Xiao .

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Editors and Affiliations

  1. University of Salford, Salford, Lancashire, UK

    Apostolos Antonacopoulos

  2. Indian Institute of Technology Bombay, Mumbai, Maharashtra, India

    Subhasis Chaudhuri

  3. Johns Hopkins University, Baltimore, MD, USA

    Rama Chellappa

  4. Chinese Academy of Sciences, Beijing, China

    Cheng-Lin Liu

  5. IIT Kharagpur, Kharagpur, West Bengal, India

    Saumik Bhattacharya

  6. Indian Statistical Institute Kolkata, Kolkata, West Bengal, India

    Umapada Pal

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Li, Y., Xiao, J. (2025). DS MYOLO: A Reliable Object Detector Based on SSMs for Driving Scenarios. In: Antonacopoulos, A., Chaudhuri, S., Chellappa, R., Liu, CL., Bhattacharya, S., Pal, U. (eds) Pattern Recognition. ICPR 2024. Lecture Notes in Computer Science, vol 15330. Springer, Cham. https://doi.org/10.1007/978-3-031-78113-1_7

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  • DOI: https://doi.org/10.1007/978-3-031-78113-1_7

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