A Cost-Effective System for Aerial 3D Thermography of Buildings

A multispectral camera in the VIS-NIR equipped with thermal imaging and environmental sensors for non invasive analysis in precision agriculture

Multispectral imaging (MSI) is a technique used to inspect materials properties in different domains, ranging from industrial to medical and cultural heritage and, recently, precision agriculture. Even though several MSI solutions are already commercially available, the research community is working to optimize multispectral cameras in terms of performance and cost. Systems for the agricultural field are usually very compact, combined with drones for large areas acquisition. In this work, we detail the implementation of an innovative, modular and low-cost solution of a multispectral camera based on three core camera systems in the optical (VIS-NIR) and thermal (LWIR) range. Multispectral imaging is performed with a rotating wheel of interchangeable band-pass filters. The system is also equipped with a set of environmental sensors to acquire CO2 concentration values, light intensity, temperature, and relative humidity of the surrounding environment. The technology and the measurement protocol were experimentally validated in laboratory and in open field. Advantages with respect to the available MSI cameras mounted on UAV is the integrated imaging in both the reflectance and the thermal emissive band in a close-up imaging setup and the use of environmental sensors. From the multispectral stack the spectral signature of the plants can be obtained and various vegetation indices (e.g., NDVI, NDRE) can be calculated for investigating the health status of the plant, while thermography provide additional monitoring. Close-up multispectral imaging is expected to tackle the new challenges of precision agriculture by enabling the acquisition of high-quality dataset on single plants.

3D Radiometric Mapping by Means of LiDAR SLAM and Thermal Camera Data Fusion

The ability to produce 3D maps with infrared radiometric information is of great interest for many applications, such as rover navigation, industrial plant monitoring, and rescue robotics. In this paper, we present a system for large-scale thermal mapping based on IR thermal images and 3D LiDAR point cloud data fusion. The alignment between the point clouds and the thermal images is carried out using the extrinsic camera-to-LiDAR parameters, obtained by means of a dedicated calibration process. Rover's trajectory, which is necessary for point cloud registration, is obtained by means of a LiDAR Simultaneous Localization and Mapping (SLAM) algorithm. Finally, the registered and merged thermal point clouds are represented through an OcTree data structure, where each voxel is associated with the average temperature of the 3D points contained within. Furthermore, the paper presents in detail the method for determining extrinsic parameters, which is based on the identification of a hot cardboard box. Both methods were validated in a laboratory environment and outdoors. It is shown that the developed system is capable of locating a thermal object with an accuracy of up to 9 cm in a 45 m map size with a voxelization of 14 cm.

Thermal endoscope based on cost-effective LWIR camera cores

The implementation of a thermal endoscope based on the LWIR camera cores Lepton and a custom miniaturized electronics is reported. The sensor and the PCB can be inserted into a cylindrical protective case of diameter down to 15mm, inox tube or plastic, 3D printable envelope, with an optical window in Germanium. Two PCBs were developed for assembling the endoscope in two different schemes, to enable frontal or lateral thermal vision setup. The thermal endoscope unit is controlled by a Raspberry external unit. The Infrared Vision Software is provided for controlling the acquisition of thermal frames, and for the thermographic calculation of the object temperature from the input parameters on object surface emissivity and environment. In general, the device enables to perform thermography in applications in which traditional larger equipment cannot be employed, as nondestructive diagnostics in confined space in the engineering field. The thermal endoscope was designed with dimensions also compatible for robotic-assisted/traditional minimally-invasive surgery.

An Instance Segmentation and Clustering Model for Energy Audit Assessments in Built Environments: A Multi-Stage Approach

Heat loss quantification (HLQ) is an essential step in improving a building's thermal performance and optimizing its energy usage. While this problem is well-studied in the literature, most of the existing studies are either qualitative or minimally driven quantitative studies that rely on localized building envelope points and are, thus, not suitable for automated solutions in energy audit applications. This research work is an attempt to fill this gap of knowledge by utilizing intensive thermal data (on the order of 100,000 plus images) and constitutes a relatively new area of analysis in energy audit applications. Specifically, we demonstrate a novel process using deep-learning methods to segment more than 100,000 thermal images collected from an unmanned aerial system (UAS). To quantify the heat loss for a building envelope, multiple stages of computations need to be performed: object detection (using Mask-RCNN/Faster R-CNN), estimating the surface temperature (using two clustering methods), and finally calculating the overall heat transfer coefficient (e.g., the U-value). The proposed model was applied to eleven academic campuses across the state of North Dakota. The preliminary findings indicate that Mask R-CNN outperformed other instance segmentation models with an mIOU of 73% for facades, 55% for windows, 67% for roofs, 24% for doors, and 11% for HVACs. Two clustering methods, namely K-means and threshold-based clustering (TBC), were deployed to estimate surface temperatures with TBC providing consistent estimates across all times of the day over K-means. Our analysis demonstrated that thermal efficiency not only depended on the accurate acquisition of thermal images but also relied on other factors, such as the building geometry and seasonal weather parameters, such as the outside/inside building temperatures, wind, time of day, and indoor heating/cooling conditions. Finally, the resultant U-values of various building envelopes were compared with recommendations from the American Society of Heating, Refrigerating, and Air-conditioning Engineers (ASHRAE) building standards.

Graphic Applications of Unmanned Aerial Vehicles (UAVs) in the Study of Industrial Heritage Assets

As a typology of cultural heritage, a wide range of singularities must to be taken into account when analysing industrial heritage assets. Graphical resources have been usually employed in heritage assets research. Nowadays, Unmanned Aerial Vehicles (UAVs) offer the possibility to obtain images of complex and difficult access areas that cannot be observed on the ground. Furthermore, aerial images allow to analyse heritage assets as a whole. Therefore, in this work, different photographs and videos has been carried out in the San Joaquin sugar cane factory and in the Aguila aqueduct, located in Nerja (Spain). These images have been used to analyse both assets as a set. Different indirect measurements on the factory chimney and irrigation water pond were taken. A Strengths, Weaknesses, Opportunities, and Threats (SWOT) about the use of UAV graphic applications in heritage assets was developed. Finally, the possibilities of conventional photograph technologies and UAV application were compared. Using UAVs allows more image acquisition possibilities than conventional systems. However, both technologies can be complemented and applied together when analysing heritage assets.