用注意力机制提升图像预测路面状况评分的准确率
An Improved ResNet50 Model for Predicting Pavement Condition Index (PCI) Directly from Pavement Images
- 在ResNet50中加入CBAM模块，自动聚焦关键路况特征
- 预测误差（MAPE）降至58.16%，优于原版ResNet50的70.76%
- 适合需要自动化路面检测的交通管理部门使用
从路面图像中准确预测路面状况指数（PCI）对基础设施维护至关重要。本研究提出一种改进的残差网络（ResNet50）架构，集成卷积块注意力模块（CBAM），直接从路面图像预测PCI，无需额外标注。通过引入CBAM，模型可自主关注图像中的关键特征，提升预测精度。相较于原始的ResNet50和DenseNet161模型，改进后的ResNet50-CBAM模型平均绝对百分比误差（MAPE）降低至58.16%，而基准模型分别为70.76%和65.48%。结果表明，注意力机制能有效优化特征提取，实现更精准、高效的路面状况评估。本研究强调了针对性特征提炼在推进自动化路面分析中的重要性。
原文摘要 · Abstract (English)
Accurately predicting the Pavement Condition Index (PCI), a measure of roadway conditions, from pavement images is crucial for infrastructure maintenance. This study proposes an enhanced version of the Residual Network (ResNet50) architecture, integrated with a Convolutional Block Attention Module (CBAM), to predict PCI directly from pavement images without additional annotations. By incorporating CBAM, the model autonomously prioritizes critical features within the images, improving prediction accuracy. Compared to the original baseline ResNet50 and DenseNet161 architectures, the enhanced ResNet50-CBAM model achieved a significantly lower mean absolute percentage error (MAPE) of 58.16%, compared to the baseline models that achieved 70.76% and 65.48% respectively. These results highlight the potential of using attention mechanisms to refine feature extraction, ultimately enabling more accurate and efficient assessments of pavement conditions. This study emphasizes the importance of targeted feature refinement in advancing automated pavement analysis through attention mechanisms.
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