Beyond Security: Leveraging Vision Solutions in Building Surveillance

Abstract

In the infrastructure industry, the focus is shifting from constructing new suburban buildings to maintaining and rehabilitating existing structures, particularly high-rise buildings. These buildings are prone to various failures due to age, density, and altitude, making regular maintenance vital for safety and longevity. However, traditional inspection methods using heavy machinery and risky rappelling are time-consuming and costly. They often fail to provide comprehensive details for inaccessible surfaces. Finding safer and more efficient inspection approaches is essential to ensure building safety and durability. We introduce an innovative end-to-end pipeline designed specifically for high-rise building inspection. Our proposed method incorporates several key components to ensure effective inspection processes. Firstly, we develop a trajectory generation system for an unmanned aerial vehicle (UAV). This system optimizes the UAV’s trajectory, enabling it to reach the desired destination even in the presence of obstacles. The trajectory can be dynamically adjusted in real-time to ensure efficient navigation. During the UAV’s flight, it captures images of the high-rise building using predetermined camera and drone parameters. These images serve as the basis for building inspections, particularly in detecting cracks. Moreover, the collected pool of images is utilized to construct a detailed 3D mesh model of the high-rise building. This model allows for a comprehensive representation of the structure, facilitating the identification and visualization of detected cracks. By combining trajectory optimization, image capture, crack detection, and 3D mesh modeling, our proposed pipeline offers a comprehensive approach to high-rise building inspection. It presents a promising solution to enhance the efficiency, accuracy, and safety of inspection processes in the field of structural engineering. Our work explores the underexplored domain of deep learning in thermalimaging. It focuses on the classical problem of generating high-resolution images from low-resolution counterparts using Superresolution (SR) techniques. The objective is to extract more details from the original scene by increasing the pixel density, which is particularly valuable in computer vision applications, including pattern recognition and medical imaging. The proposed pipeline aims to achieve real-time video super-resolution using a thermal camera on an embedded edge device.