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Deep Neural Network for Structural Prediction and Lane Detection in Traffic Scene.

Jun Li, Xue Mei, Danil Prokhorov

    IEEE Transactions on Neural Networks and Learning Systems
    |February 19, 2016
    PubMed
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    This study introduces novel deep neural networks that incorporate spatial structure for improved image analysis. These networks effectively detect lane boundaries in traffic scenes, even in unmarked areas.

    Area of Science:

    • Computer Vision
    • Deep Learning
    • Artificial Intelligence

    Background:

    • Hierarchical neural networks excel at feature learning but often neglect spatial structures in image analysis.
    • Understanding scene context requires analyzing the spatial distribution of visual cues.
    • Existing methods struggle with classifying objects without considering their spatial relationships.

    Purpose of the Study:

    • To extend deep neural networks by integrating structural and spatial cues for enhanced visual analysis.
    • To propose and evaluate novel network architectures for structured visual detection.
    • To improve the accuracy and robustness of object detection, specifically lane boundary detection in traffic scenes.

    Main Methods:

    • Developed a multitask deep convolutional network for simultaneous target presence and geometric attribute detection (location, orientation).

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  • Introduced a recurrent neural network layer for structured visual detection, adept at handling objects with undefined shapes.
  • Applied both proposed networks to the practical task of detecting lane boundaries in complex traffic scenes.

    • Main Results:

    • The multitask convolutional neural network provided valuable auxiliary geometric information for lane structure modeling.
    • The recurrent neural network successfully detected lane boundaries, including unmarked areas, without prior knowledge or secondary modeling.
    • Both network architectures demonstrated significant improvements in handling spatial structures for visual detection tasks.

    Conclusions:

    • Integrating spatial structural cues into deep neural networks significantly enhances visual analysis capabilities.
    • The proposed multitask and recurrent networks offer robust solutions for structured visual detection, particularly in challenging environments like traffic scenes.
    • This research advances the field of deep learning for scene understanding by effectively leveraging spatial relationships within visual data.