Pavement Crack Detection Algorithm Based on Densely Connected and Deeply Supervised Network
Main Article Content
Abstract
Road cracks may form as a result of climatic changes and low-quality building materials, which is important for maintenance as well as the fact that continual exposure would seriously harm the environment. The automated identification and classification of fractures in the surface of a road's paving, without the need for manually labelled samples. This idea seeks to reduce human subjectivity that results from conventional visual surveys. The fracture identification process is initially carried out using learning from example photos extracted from the dataset. Non-overlapping picture blocks are categorised by the algorithm as either having crack pixels or not. The road photos can be used to find fractures in the pavement. With the aid of feature extraction from the photos, a supervised model is created for the detection of fractures in road photographs. The features were extracted, normalised, and then classified as either crack or non-crack based on the feature values produced. The picture is classified as cracked or not cracked based on the characteristics that were retrieved. During the testing phase, the pictures' characteristics were retrieved and image fractures were located. By comparing the retrieved feature values with the suggested threshold, the fractures were then further divided into several categories. Finally, the process' performance is assessed.
Downloads
Metrics
Article Details
Licensing
TURCOMAT publishes articles under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This licensing allows for any use of the work, provided the original author(s) and source are credited, thereby facilitating the free exchange and use of research for the advancement of knowledge.
Detailed Licensing Terms
Attribution (BY): Users must give appropriate credit, provide a link to the license, and indicate if changes were made. Users may do so in any reasonable manner, but not in any way that suggests the licensor endorses them or their use.
No Additional Restrictions: Users may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.