This article proposes a method for damage detection using vision-based monitoring with motion magnification technique. The methods based on the vibration characteristics of structures such as natural frequency, mode shapes, and modal damping have been applied to structural damage detection. However, the conventional methods have limitations for practical applications. Vision-based monitoring system can be employed as a new structural monitoring system because of its simplicity, potentially low cost, and unique capability of collecting high-resolution data. A methodology called video motion magnification has been developed to amplify non-visible small motions in a video to reveal the dynamic response. The video motion magnification method can be applied to measure small displacements to calculate the natural frequencies and the operational deflection shapes of the structures. Unlike conventional optimization methods, a genetic algorithm explores the entire solution space and can obtain the global optimum. In this article, identification of the location and magnitude of damage in a cantilever beam is formulated as an optimization problem using a real-value genetic algorithm by minimizing the objective function, which directly compares the first three natural frequencies changes from the phase-based motion magnification measurement and from the analytical model of a damaged cantilever beam.

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This paper proposes a high-performance and low-cost in-flight collision avoidance system based on background subtraction for unmanned aerial vehicles (UAVs). The pipeline of proposed in-flight collision avoidance system is as follows: (i) dynamic background subtraction to remove the background and to detect moving objects, (ii) denoise using morphology and binarization methods, (iii) Euclidean clustering to cluster the moving object and to remove noise blobs, (iv) distinguish independent objects and track the movement using Kalman filter, and (v) collision avoidance using proposed decision-making techniques. This work focuses on the design and the demonstration of a vision-based fast moving object detection and tracking system with decision-making capabilities to perform evasive maneuvers to replace high vision system such as event camera. The development of high-performance, low-cost unmanned aerial vehicles paired with rapid progress in vision-based perception systems herald a new era of autonomous flight systems with mission-ready capabilities. One of the key features of an autonomous UAV is a robust mid-air collision avoidance strategy. The novelty of our method lies in the motion-compensating moving object detection framework, which accomplishes the task with background subtraction via 2-D transformation approximation. Clustering and tracking algorithms process detection data to track independent objects, and stereo-camera-based distance estimation is conducted to estimate the 3-D trajectory, which is then used during decision-making procedures. The examination of the system is conducted with a quadrotor UAV test vehicle, and appropriate algorithm parameters for various requirements are deduced.

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