# Wind Speed Measurement via Visual Recognition of Wind-Induced Waving Light Stick Target
## 1 Introduction  
Computer vision is a multidisciplinary field of study that involves enabling computers to gain understanding of data (usually digital images and videos) and, in the process, acquire, process, and extract useful information for decision making [1]. Some of the popular areas of application of computer vision techniques include robotics [2], autonomous vehicles [3], and environmental monitoring [4]. The measurement of wind speed and direction is a crucial aspect of environmental monitoring, especially for areas such as fault diagnosis [5] and wind energy monitoring [6]. Meteorologists also use wind information for climate pattern and climate trend research [6]. While the accuracy of the technique employed in measuring this highly dynamic parameter (wind speed) is central to the integrity of the measurement, factors such as cost and area of application can lead to differences in choosing an appropriate technique, since different techniques may have application-specific strengths and weaknesses [7]. One application where cup anemometers may be more suitable than pit tube anemometers is in measuring wind speeds near the ground or in complex terrain [8]. This is because cup anemometers are less affected by turbulence and changes in wind direction than pitot tube anemometers, which can be affected by eddies and vortices in the flow [9, 10]. On the other hand, pitot tube anemometers may be more suitable for measuring airspeed in aircraft, unmanned aerial vehicles (UAVs), orother fluid flows where the total velocity, including horizontal and vertical components, is of interest [11]. Therefore, computer vision hardware and algorithms similarly offer a strong alternative to other wind measurement systems, alongside the expected comparative application-specific merits. They are usually applied in areas where highly accurate, cost-effective, real-time, and non-intrusive approaches are desired [12; 13]. Following fundamental contributions such as the simple mechanical wind indicator developed by [PERSON] (1635-1703) and Sir [PERSON] (1632-1723), which measured wind speed based on the directional deflection of a hanging plate oriented into the direction from which the wind was blowing [14], more complex mechanical and ultrasonic wind sensors were developed. The cup anemometer for instance, has become a widely used device for accurate horizontal wind speed measurement, improving upon the previous techniques [15]. The use of pitot tubes in airspeed measurement has also gained prominence because of their advantages in measuring fluid flow where the use of anemometers is impractical, in addition to their characteristic low cost, minimal friction losses, simple set up, and ease of installation merits [16; 17]. In the aviation industry and wind tunnel ducts, pitot tubes can measure static and dynamic pressure of the prevailing wind, which is then converted to airspeed using the Bernoulli equation. Despite their many advantages however, they are sensitive to wind flow direction, can become clogged by particles when operating in dirty environments, and do not work well in low or medium wind speed environments [18]. In recent years, computer vision techniques have increasingly been used for wind measurement due to their accuracy, efficiency, and cost effectiveness. Existing studies have considered employing machine learning techniques such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for wind measurement [19; 20; 21; 22]. Notably, [PERSON] et al. [21] proposed a novel wind measurement system for an unmanned sailboat based on computer vision (CV). The system consists of an airflow rope, a camera, and a computing platform. It measures the wind speed and direction by analyzing the fluttering of the airflow rope using a combination of a CNN and an RNN. Other methods which do not rely on artificial intelligence have also been reported [19; 20; 21]. Among these, the work by [PERSON] et al. [22] is notable.