Image Processing

Monitoring of Construction Equipment

Construction sites are dynamic environments that require efficient use of construction equipment. Accurate tracking of the locations and condition of the equipment in use is essential here, as this information enables more precise coordination and planning of construction operations. This can increase efficiency and productivity by optimizing the utilization of construction equipment through the reduction of idle time. Construction managers can use this information to obtain a comprehensive view of the current status of equipment in order to make informed decisions. Artificial intelligence methods are used to provide this information by evaluating images and sensor data and making them available to site management.

Damage Detection and Documentation

Manual damage recording during structural inspections is a time-consuming and error-prone process. Additionally, inconsistencies in damage documentation arise due to variations in the styles adopted by individual inspectors. In response to these challenges, this research area investigates how to realize automated damage recording with AI. In particular, the use of cameras for image-based damage recording plays a central role therein. Automatic evaluation of images using AI provides precise damage type and geometry determination. This automatic recording reduces the inspection effort. In addition, a correct quantification and evaluation of the damage ensures a reliable assessment of the damage degree and necessary repair measures.

Visual Fire Safety Inspection

Automatic detection and inspection of fire protection equipment (FSE) in images represents a promising research area, especially in the field of facility management. Using computer vision and innovative machine learning techniques, visual data can be efficiently analyzed to provide numerous clues regarding the inspection and maintenance of FSE. This information can then be used to update BIM models and determine the need for additional fire protection measures. The goal of this research is to automate the inspection and maintenance of FSE and thus reduce the workload of fire inspectors during a fire protection inspection.