基于Faster R-CNN与形态法的路面病害识别

为提高基于图像处理的路面表观病害检测识别效率及精度,引入目标检测中的快速区域卷积神经网络(Faster Region Convolutional Neural Network,Faster R-CNN)算法以快速识别病害种类、位置与面积;针对已提取的带边框裂缝病害区域,采用基于VGG16迁移学习与模型微调的CNN与50...

Pavement Distress Detection Based on Faster R-CNN and Morphological Operations

To improve the efficiency and accuracy of image-based pavement distress detection as well as quickly identify the type, location, and magnitude of the distress, the Faster R-CNN algorithm for object detection is introduced. A convolutional neural network (CNN) based on VGG16 migration learning and model fine tuning was employed in an extracted crack area with a bounding frame to locate the crack skeleton with a 50% overlap sliding window. Then, morphological operations were conducted to extract the crack skeleton and calculate its length and width. To improve the performance of Faster R-CNN and evaluate the effectiveness of the integrated algorithms where a low misdetection rate but high false-detection rate is likely, the precision, recall, and F₁ score were introduced. The maximum F₁ score was used to determine the pixel area of the distress frame and the corresponding confidence threshold thus reducing the false detection rate and adapting to the diverse scenarios of pavement surface distress. The rapid pavement distress detection algorithm was applied to an expressway in Guangdong, China. The test results on typical crack sample images show that the proposed method is more efficient than full-field image processing methods such as CNN with sliding window and traditional morphology operations. As the segmented crack bounding frames were merged and adjusted, and the optimized pixel area and confidence threshold for the distress boxes were considered. It was observed that the precision rate of the transverse crack increases from 0.861 to 0.918, whereas the false detection rates of the horizontal and vertical cracks decrease significantly from 20.4% and 23.8% to 8.2% and 6.9% before and after adjustment, respectively. The proposed pavement distress detection method integrating Faster R-CNN, CNN, and morphological operations has the advantages of high efficiency and low misdetection rate. Moreover, the false detection rates are greatly reduced by introducing the evaluation method and the thresholds of pixel area and confidence value, which indicates the engineering application potential of the proposed method.