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Hao Sheng

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Proceedings Article | 7 December 2023 Paper

A dense convolutional network lane detection based on attention mechanism

Xiangzhi Kong, Hao Sheng, Tong Zhang, Qingguo Sun, Honglin Wan

Proceedings Volume 12941, 1294156 (2023) https://doi.org/10.1117/12.3011455

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Lane detection is a fundamental component of Advanced Driver Assistance Systems (ADAS). In this paper, we propose a dense convolutional network lane detection based on attention mechanism. The algorithm adopts DenseNet (Dense Convolutional Network) as the backbone network to capture global features and improve detection accuracy. In addition, CBAM is utilized to enhance the perception of lane details. Moreover, an auxiliary training network is designed where Spatial Pyramid Pooling (SPP) is employed to obtain multi-scale feature representations for handling lanes in various sizes and shapes. Our algorithm effectively addresses the issues of efficiency and challenging scenarios like no-visual cues. Experiments on public data sets show that the detection accuracy of our proposed algorithm improves to 96.9% compared with other state-of-the-art algorithms, especially under complex lighting conditions.

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