多尺度特征增强的路面裂缝检测方法

针对路面裂缝检测不完整和分割出现断裂的问题，提出了一种多尺度特征增强的路面裂缝检测网络MFENet，实现端到端的路面裂缝图像检测、分类和分割处理；设计了多尺度注意力特征增强模块，建立了网络模型的上层多尺度特征通道与底层特征通道权重系数之间的映射关系，以提升有效通道的特征输出；基于路面裂缝的坐标信息和像素语义信息在物理位置上的相关性，设计了多语义特征关联模块，实现不同语义信息之间的特征融合增强，并通过特征维度转换实现对路面裂缝图像的前景特征过滤；提出了一种针对深度特征强度进行量化评估的方法，用于提升模型提取特征能力的可解释性。在自采集数据集上的研究结果表明：MFENet对路面裂缝图像检测的平均精准率和平均召回率相比Mask R-CNN分别提升了4.3%和5.4%，相比基线模型RDSNet分别提升了14.6%和14.3%；MFENet对路面裂缝图像分割的平均精准率和平均召回率相比Mask R-CNN分别提升了6.6%和8.8%，相比RDSNet分别提升了8.1%和9.7%；与Mask R-CNN等主流方法相比，MFENet对不同类型路面裂缝图像的检测、分割精度最高。在公开数据集(CFD、CRACK500)上的研究结果表明：在不同场景下的数据集上，MFENet的检测、分割精度均高于Mask R-CNN等主流方法，模型的鲁棒性更强。另外与RDSNet相比，MFENet在不同数据集上的处理速度也均有所提升。

Pavement crack detection method based on multi-scale feature enhancement

To solve the problems of incomplete pavement crack detection and discontinuous segmentation, a detection network MFENet for pavement cracks based on multi-scale feature enhancement was proposed, and the detection, classification and segmentation of end-to-end pavement crack images were realized. A multi-scale attention-based feature enhancement module was designed, and the mapping relationships of the weight coefficients of the upper multi-scale feature channels with those of the lower feature channels in the network model were determined to highlight the feature outputs from the effective channels. Based on the correlation between the coordinate information of the pavement crack and the semantic information of the pixels in physical location, a multi-semantic feature correlation module was designed and thereby feature fusion and enhancement among different semantic information were achieved. Then, the foreground features of the pavement crack image were filtered by feature dimension transformation. A quantitative evaluation method for deep feature intensity was proposed to improve the interpretability of the model's feature extraction ability. Research results on self-collected dataset show that the average precision and average recall of the MFENet in pavement crack image detection are 4.3% and 5.4% higher than those of the Mask R-CNN, respectively, and 14.6% and 14.3% higher than those of the baseline model RDSNet, respectively. The average precision and average recall of the MFENet in pavement crack image segmentation are 6.6% and 8.8% higher than those of the Mask R-CNN, respectively, and 8.1% and 9.7% higher than those of the RDSNet, respectively. In the comparison with the Mask R-CNN and other mainstream methods, the images of different types of pavement cracks are detected and segmented with the highest accuracy by the MFENet. Research results on public datasets (CFD and CRACK500) show that the detection and segmentation accuracy of the MFENet are invariably higher than those of the Mask R-CNN and other mainstream methods on the datasets covering different scenarios, indicating the higher robustness of the proposed method. In addition, the processing speed of the MFENet is also faster than that of the RDSNet on different datasets.