Automated detection of road defects has historically been challenging for the pavement management industry. As a result, new methods have been developed over the past few years to handle this issue. Most of these methods relied on supervised machine learning techniques, such as object detection and segmentation methods, which need a large, annotated image dataset to train their models. However, annotating pavement defects is difficult and time-consuming due to their ununiformed and complex shapes. To address this challenge, a hybrid pavement defect classification and localization framework using weakly supervised and supervised deep learning methods is proposed in this thesis. This framework has two steps: (1) A robust hierarchical two-level classifier that classifies the defects in images, and (2) A method for defect localization combining weakly supervised and supervised techniques. In the localization method, first, defects are primarily localized using a weakly supervised method (i.e. Class Activation Mapping (CAM)). Next, based on the results of the first classifiers, the defects are segmented from the localized patches obtained in the previous step. The feature maps extracted from the CAM method are used to train a segmentation network once (i.e. U-Net or Mask R-CNN) to localize and segment the defects in the images. Thus, the proposed framework combines the advantages of weakly supervised and supervised methods. The supervised modules in the framework are trained once and can be used for any new data without the need to train. In other words, to use our framework on new dataset only the classifiers should be fine-tuned. Furthermore, the proposed framework introduced an innovative method designed to calculate the maximum crack width in pixels within linear segmented defect patches, derived from the localization module of the proposed framework. This method is particularly advantageous as it provides critical information that can be further employed in the calculation of the Pavement Condition Index (PCI).
Additionally, the proposed method benefits from an asset management inspection system based on Geographic Information System (GIS) technology to prepare the dataset used in the training and testing. Thus, this advanced system serves a dual role within our framework. Firstly, it assists in the assembly and preparation of the dataset used in the model training process, providing a geographically organized collection of images and related data. Secondly, it plays a crucial role in the testing phase, offering a spatially accurate platform for evaluating the effectiveness of the model in real-world scenarios.
A dataset from Georgia State in the USA was used in the case study. The proposed framework obtained high precision of 97%, 88%, 92% and 97% for localizing the alligator, block, longitudinal and transverse cracks, respectively. Considering all factors, such as annotation cost, and performance on the test dataset, the proposed localization method outperforms the supervised localization methods, such as instance segmentation and object detection for localizing road pavement defect
