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Small Object Sensitive Segmentation of Urban Street Scene with Spatial Adjacency Between Object Classes.

Dazhou Guo, Ligeng Zhu, Yuhang Lu

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
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    This study introduces a novel boundary-based metric to improve deep learning-based urban street scene segmentation, significantly enhancing the accurate segmentation of small objects like poles and signs.

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    Area of Science:

    • Computer Vision
    • Deep Learning
    • Artificial Intelligence

    Background:

    • Deep learning excels in urban street scene segmentation but struggles with small objects due to size-induced biases in segmentation loss.
    • Accurate segmentation of small objects like poles and traffic signs is crucial for autonomous driving and urban planning.

    Purpose of the Study:

    • To develop a novel boundary-based metric to address the under-segmentation of small objects in urban street scenes.
    • To integrate this metric into a deep learning framework for improved segmentation accuracy.

    Main Methods:

    • A new boundary-based metric was proposed to quantify spatial adjacency between object classes, proving robust against object size biases.
    • A novel network architecture was developed, incorporating an encoder to compute the boundary metric and training the segmentation network end-to-end.
    • The trained segmentation network is used for inference, excluding the encoder for efficiency.

    Main Results:

    • The proposed method demonstrated favorable overall performance improvements on the CamVid and CityScapes datasets.
    • A substantial enhancement in segmenting small objects was achieved, addressing a key limitation of existing methods.
    • The boundary-based metric effectively mitigated biases caused by object size variations.

    Conclusions:

    • The proposed boundary-based metric and network integration offer a significant advancement in urban street scene segmentation.
    • This approach effectively improves the segmentation of small, often overlooked objects, leading to more comprehensive scene understanding.
    • The method shows strong potential for applications requiring precise scene analysis, such as autonomous navigation and intelligent transportation systems.