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Exploring the Impact of Deep Learning Models on Lane Detection Through Semantic Segmentation

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Abstract

Due to advancements in the deep learning technology, object detection has become significantly important for lane detection and vehicle detection. In recent times, lane detection has become more popular as it plays a significant role in traffic surveillance compared to other object detection technology. However, these strategies have several intrinsic flaws which need to be addressed. Traditional-based techniques still suffer from the challenges of the effectiveness and accuracy, whereas a complex convolutional layer is a challenge for deep learning-based strategies. A parameter selection issue affects the majority of the available lane detection algorithms, which further contributes to their unsatisfactory detection performance. In this study, we provide an effective lane detection method based on semantic segmentation to identify lane lines in a high-dimensional dataset by adding vertical spatial properties and contextual driving information. This paper employs two created frames—feature merging block and information exchange block—to identify unclear and obstructed lane lines more effectively. The simulations have been carried out for the proposed model on TUSimple and CULane datasets which resulted with 94.42% accuracy, 93.47% precision, 92.85% recall, and 93.17% F1-score.

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Authors and Affiliations

  1. School of Computing Science and Engineering, Galgotias University, Greater Noida, India

    Sunil Kumar

  2. Department of Computer Science & Engineering, Manipal University Jaipur, Jaipur, India

    Ankur Pandey

  3. Department of Computer Science & Engineering, Sharda School of Engineering & Technology, Greater Noida, India

    Sudeep Varshney

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  1. Sunil Kumar
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  2. Ankur Pandey
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  3. Sudeep Varshney
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Correspondence to Sunil Kumar.

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Kumar, S., Pandey, A. & Varshney, S. Exploring the Impact of Deep Learning Models on Lane Detection Through Semantic Segmentation. SN COMPUT. SCI. 5, 139 (2024). https://doi.org/10.1007/s42979-023-02328-5

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