【摘要】 路面抗滑性能受路面宏观纹理和微观纹理共同影响，为了实现车载激光扫描设备快速收集高分辨率三维路面纹理，以及路面抗滑性能的连续非接触式微观尺度测评，构建了基于自监督深度学习的超分辨率网络模型，循环递归地将不同低分辨率的路面纹理沿行车方向重构至0.1 mm精度。将527幅0.1 mm分辨率的SMA-13沥青路面纹理用于网络训练与测试，利用最邻近法分别以1/2、1/4、1/8、1/16的下采样因子模拟不同车速下的低分辨率纹理。将分辨率相差2倍的同种纹理对作为网络模型的输入，并按8∶2比例随机划分训练集和测试集，训练集通过小尺寸分割进行数据增强。在最优权重下，模型能够循环递归地将不同低分辨率的路面纹理重构至0.1 mm·像素^-1分辨率。利用峰值信噪比（Peak Signal-to-Noise Ratio, PSNR）、结构相似度（Structural Similarity, SSIM）和平均构造深度相对误差评估超分辨率纹理的重构质量，并以双三次插值法作为对照。结合双三次插值法与所提出的超分辨率网络，研究非预设下采样因子模型对低分辨率路面纹理的重构性能。最后，基于OOA-LightGBM算法，提取高分辨率纹理以及超分辨率重构纹理的特征参数，构建抗滑性能预测模型，研究基于超分辨率重构纹理预测抗滑性能的可能性。结果表明：所提出的超分辨率网络有助于车载激光扫描设备在128 km·h^-1的行驶速度内采集PSNR大于30 dB、SSIM大于0.95、平均构造深度相对误差绝对值小于1%的0.1 mm高精度路面三维纹理；在16、32、64 km·h^-1的恒定行驶速度下收集的路面三维纹理，其抗滑性能预测的平均决定系数R²为0.808。更多还原

【Abstract】 The skid resistance of pavement surfaces is influenced by both macro-texture and micro-texture.To achieve high-resolution 3D pavement texture acquisition using vehicle-mounted laser scanning equipment and enable continuous, non-contact microscale evaluation of pavement skid resistance, this study constructed a super-resolution network model based on self-supervised deep learning. The model recursively reconstructed low-resolution pavement textures to a 0.1 mm·pixel^-1 resolution in the driving direction. A total of 527 SMA-13 asphalt pavement textures with a resolution of 0.1 mm were prepared for training and testing. The nearest-neighbor method was used to downsample the textures by factors of 1/2, 1/4, 1/8, and 1/16 to simulate low-resolution textures captured at different vehicle speeds. Texture pairs with a two-fold resolution difference were used as input for the network model, and the dataset was randomly split into training and test sets in an 8∶2 ratio. The training set was augmented by small-scale segmentation. Under optimal weights, the model recursively reconstructed various low-resolution pavement textures to a 0.1 mm·pixel^-1 resolution. The reconstruction quality of the super-resolution textures was evaluated using Peak Signal-to-Noise Ratio（PSNR）, Structural Similarity（SSIM）, and the relative error of mean texture depth, with bicubic interpolation as a comparison. By integrating bicubic interpolation with the proposed super-resolution network, this study investigated the reconstruction performance of low-resolution pavement textures under non-predefined downsampling factors. Finally, using the OOA-LightGBM algorithm, feature parameters of the high-resolution and super-resolution reconstructed textures were extracted to build a skid resistance prediction model, exploring the feasibility of predicting skid resistance based on super-resolution reconstructed textures. Results show that the proposed super-resolution network enables the vehicle-mounted laser scanning device to collect 3D pavement textures with a resolution of 0.1 mm, a PSNR greater than 30 dB, an SSIM above 0.95, and an absolute relative error in mean texture depth（MTD） of less than 1%, within a speed of 128 km·h^-1. For pavement textures collected at constant speeds of 16 km·h^-1, 32 km·h^-1, and 64 km·h^-1, the average coefficient of determination（R²） for skid resistance prediction was 0.808.更多还原