Concrete Bridge Crack Segmentation using Deep Learning
Disciplines
Artificial Intelligence and Robotics | Civil Engineering | Data Science
Abstract (300 words maximum)
This research project tackles the inefficiencies and limitations inherent in conventional bridge inspection methods, particularly the challenges of detecting structural cracks. Traditional approaches are characterized by their labor-intensive nature and significant time and cost requirements and often necessitate temporary bridge closures, causing traffic disruptions and inconvenience. This study proposes an innovative solution that integrates advanced computer vision techniques with drone technology to revolutionize the bridge inspection process. In addressing the critical need for safety and maintenance in transportation infrastructure, this research adopts a methodology that leverages drones equipped with high-resolution cameras. These drones are utilized to capture comprehensive aerial imagery of bridges, focusing on identifying cracks and structural deficiencies. The collected data undergoes processing to support accurate and efficient crack detection, serving as a vital tool for maintenance and safety decision-making. The findings from this study indicate that adopting this automated crack detection system significantly improves the precision and reliability of bridge inspections. By offering infrastructure managers detailed insights into the condition of bridges, including the presence and severity of cracks, the proposed approach not only enhances the safety and durability of bridges but also streamlines the inspection process. This advancement allows transportation authorities to bypass the drawbacks of traditional methods, achieving cost savings and reducing operational disruptions. This research stands as a landmark in the application of technological innovation to bridge safety, marking a significant leap forward in the domain of infrastructure maintenance and management.
Academic department under which the project should be listed
SPCEET - Civil and Environmental Engineering
Primary Investigator (PI) Name
Da Hu
Concrete Bridge Crack Segmentation using Deep Learning
This research project tackles the inefficiencies and limitations inherent in conventional bridge inspection methods, particularly the challenges of detecting structural cracks. Traditional approaches are characterized by their labor-intensive nature and significant time and cost requirements and often necessitate temporary bridge closures, causing traffic disruptions and inconvenience. This study proposes an innovative solution that integrates advanced computer vision techniques with drone technology to revolutionize the bridge inspection process. In addressing the critical need for safety and maintenance in transportation infrastructure, this research adopts a methodology that leverages drones equipped with high-resolution cameras. These drones are utilized to capture comprehensive aerial imagery of bridges, focusing on identifying cracks and structural deficiencies. The collected data undergoes processing to support accurate and efficient crack detection, serving as a vital tool for maintenance and safety decision-making. The findings from this study indicate that adopting this automated crack detection system significantly improves the precision and reliability of bridge inspections. By offering infrastructure managers detailed insights into the condition of bridges, including the presence and severity of cracks, the proposed approach not only enhances the safety and durability of bridges but also streamlines the inspection process. This advancement allows transportation authorities to bypass the drawbacks of traditional methods, achieving cost savings and reducing operational disruptions. This research stands as a landmark in the application of technological innovation to bridge safety, marking a significant leap forward in the domain of infrastructure maintenance and management.