## 1 Introduction
This can lead to situations where conventional detection methods struggle to detect drones effectively. In addition, various electromagnetic noises and clutter in the environment may obscure drone signals, further complicating detection. Another method for aerial surveillance involves using acoustic characteristics to detect drones. [PERSON] et al. [10] proposed a technique that identifies the presence of drones by visualizing the spectrum of acoustic signals generated during flight using fast Fourier transform and recognizing specific frequency patterns. This method leverages the unique frequency characteristics of drone flight noise for detection. [PERSON] et al. [11] transformed acoustic signals into spectrograms using short-time Fourier transform and trained convolutional neural networks (CNNs) to effectively distinguish between drone and non-drone signals. This approach uses the time-frequency information of acoustic signals and the robust recognition capabilities of CNNs to improve drone identification accuracy in diverse environments. However, acoustic sensor-based detection methods are highly susceptible to external factors such as noise, wind, and weather conditions, which can significantly degrade detection performance. In addition, drones at long distances generate weaker acoustic signals, making detection challenging. Variations in flight patterns or environmental conditions can also affect the acoustic signal characteristics, posing additional limitations. Vision sensors have been employed to detect low-altitude drones by providing high-resolution imagery, enabling the identification of fine details, real-time monitoring, and flight path analysis. However, unlike radar, vision sensors face challenges in identifying small drones from long distances due to reduced image resolution. To address these issues, research has focused on improving detection performance through image grid partitioning and resolution enhancement. [PERSON] et al. [12] divided the image into grids and used the object detection model you only look once (YOLO) to independently process each grid area, making it advantageous for detecting small drones at long distances through image magnification. [PERSON] et al. [13] introduced an adaptive slicing technique to dynamically divide images into various sizes and allocate more computational resources to critical areas, thereby improving drone detection efficiency. They also developed hyper inference to accurately deduce drone characteristics and locations. However, these methods typically require high-performance hardware for handling high-dimensional data, limiting their real-time processing capabilities. [PERSON] et al. [14] addressed these challenges by enhancing feature extraction using residual feature aggregation to mitigate gradient vanishing issues even in deeper neural networks. They also employed generative adversarial networks to model diverse scenarios for detecting small drones under various environmental conditions, thereby improving the detection effectiveness and accuracy. [PERSON] [15] utilized fast super-resolution CNN (SRCNN) and SRCNN to enhance the recognition of fine details in small drones and proposed a method to reduce false detections using a single-shot multibox detector. [PERSON] et al. [16] improved the detection performance for small objects by adaptively learning various object characteristics and proposed a method to detect small objects effectively using SRCNN. Although these studies present diverse approaches to improve detection performance for small objects or drones, challenges such as artificial noise or distortions introduced during resolution enhancement and high computational demands remain unresolved, limiting their real-time detection capabilities. To overcome these challenges and meet the requirements of real-time aerial surveillance, this study proposes a vision sensor-based detection framework called the noise suppression super-resolution detector (NSSRD).