Faris Azhari
Papers
1
Total Citations
6
H-Index
1
About
Faris Azhari is a researcher focused on the intersection of computer vision, deep learning, and structural health monitoring, with a particular emphasis on automated crack detection in complex, unstructured environments. His most cited work, "PointCrack3D: Crack Detection in Unstructured Environments using a 3D-Point-Cloud-Based Deep Neural Network" (2021), introduces a novel deep neural network that processes 3D point cloud data to identify surface cracks on buildings, natural rock walls, and underground mine tunnels. This contribution is significant because it addresses a critical safety challenge: timely detection of structural integrity issues that can threaten both infrastructure and human life. By leveraging 3D data rather than traditional 2D images, Azhari’s approach improves detection accuracy in irregular, real-world settings where lighting and surface conditions vary. Although his citation count is currently modest (6 citations for this paper), the work represents a foundational step toward more robust, automated inspection systems. His research holds promise for applications in civil engineering, mining safety, and disaster response, positioning him as an emerging voice in the field of intelligent infrastructure monitoring.
Research Focus
Key Achievements
Top Papers
- 1