Gu Xiang, Huang Qiwei, Du Chaonan
School of Artificial Intelligence and Computer Science, Nantong University, Nantong 226019, China.
School of Information Science and Technology, Nantong University, Nantong 226019, China.
Sensors (Basel). 2024 Dec 19;24(24):8098. doi: 10.3390/s24248098.
With the growing prominence of autonomous driving, the demand for accurate and efficient lane detection has increased significantly. Beyond ensuring accuracy, achieving high detection speed is crucial to maintaining real-time performance, stability, and safety. To address this challenge, this study proposes the ECBAM_ASPP model, which integrates the Efficient Convolutional Block Attention Module (ECBAM) with the Atrous Spatial Pyramid Pooling (ASPP) module. Building on traditional attention mechanisms, the ECBAM module employs dynamic convolution kernels to eliminate dimensionality reduction, enhancing the efficiency of feature channel learning and local interactions while preserving computational efficiency. The ECBAM_ASPP model incorporates the ECBAM attention mechanism into the feature extraction network, effectively directing the network to focus on salient features while suppressing irrelevant ones. Additionally, through variable sampling of the input, the model achieves multi-scale feature extraction, enabling it to capture richer lane-related feature information. Experimental results on the TuSimple and CULane datasets demonstrate that the ECBAM_ASPP model significantly improves real-time performance while maintaining high detection accuracy. Compared with baseline methods, the proposed model delivers superior overall performance, showcasing greater robustness and practicality.
随着自动驾驶的日益突出，对准确高效的车道检测的需求显著增加。除了确保准确性外，实现高检测速度对于保持实时性能、稳定性和安全性至关重要。为应对这一挑战，本研究提出了ECBAM_ASPP模型，该模型将高效卷积块注意力模块（ECBAM）与空洞空间金字塔池化（ASPP）模块相结合。基于传统注意力机制，ECBAM模块采用动态卷积核来消除降维，在保持计算效率的同时提高特征通道学习和局部交互的效率。ECBAM_ASPP模型将ECBAM注意力机制纳入特征提取网络，有效地引导网络关注显著特征，同时抑制无关特征。此外，通过对输入进行可变采样，该模型实现了多尺度特征提取，使其能够捕获更丰富的车道相关特征信息。在TuSimple和CULane数据集上的实验结果表明，ECBAM_ASPP模型在保持高检测准确率的同时显著提高了实时性能。与基线方法相比，所提出的模型具有卓越的整体性能，展现出更强的鲁棒性和实用性。
