有效特征提取和级联优化的路面裂缝检测方法
Research on Highway Pavement Crack Detection Method based on Efficient Multi-scale Feature Combining and Hierarchical Boosting Model
-
摘要: 为了提高公路路面裂缝的检测精度, 针对裂缝的多态性和噪声干扰等问题, 提出一种结合有效多尺度特征融合结构和级联优化的裂缝检测方法. 首先构建有效多尺度特征融合提取结构, 增强对不同尺度裂缝特征的提取效果; 然后针对编码器-解码器对于特征信息的关注重点, 分别引入空间和通道注意力机制抑制背景噪声干扰, 增强裂缝特征表达; 最后对不同层级解码特征进行联合优化学习, 强化对低层级特征信息的利用, 提升裂缝分割纹路的连贯性. 在Crack500等数据集上进行实验的结果表明, 所提方法检测出的可视化裂缝结果纹理清晰完整, 在综合测试集上F1分数与平均交并比为90.07%和82.07%, 具有更好的识别效果和鲁棒性, 可为自动化裂缝缺陷检测任务提供一种深度学习方法.Abstract: To improve the detection accuracy of highway pavement cracks, a crack detection method combining ef-fective multi-scale feature fusion structure and cascade optimization is proposed for the problems of crack polymorphism and noise interference. Then, aiming at the focus of encoder-decoder on feature information, spatial and channel attention mechanisms are introduced to suppress background noise interference and enhance crack feature expression. Finally, the joint optimization learning of different levels of decoding features is carried out to strengthen the use of low-level feature information and improve the coherence of crack segmentation lines. The experimental results on data sets such as Crack500 show that the visual crack results detected by the proposed method have clear and complete texture, and the F1 score and average in-tersection over union on the comprehensive test set are 90.07% and 82.07%, which has better recognition effect and robustness, and can provide a deep learning method for automatic crack defect detection tasks.