# A Semantic Segmentation Dataset and Real-Time Localization Model for Anti-UAV Applications
## 1 Introduction  
The use and application of unmanned aerial vehicles (UAVs) have gained significant attention in recent years, driven by rapid advancements in UAV technology. Currently, UAVs are widely adopted in the consumer industry for purposes such as videography, content creation, and event-based filmmaking. Several companies are also promoting the use of UAVs for logistics and transportation, although such services are still limited to specific regions [1, 2, 3]. The evolution of UAVs into more compact, affordable, and easily controllable devices has facilitated their adoption among end-users [4, 5]. However, this ease of access presents security and privacy concerns, as UAVs may violate public or personal safety [6, 7, 8]. This is primarily due to the difficulty in regulating and controlling individual users. Moreover, the trend toward smaller UAVs exacerbates the problem, as such drones are more difficult to detect and localize. Consequently, it is critical to develop systems capable of reliably detecting and tracking nearby UAVs to safeguard against unauthorized intrusions. Currently, the technologies for detecting illegally operating UAVs remain incomplete and unreliable [9]. Several methods have been proposed, most of which utilize radar [10], radio frequency (RF) signals [11, 12, 13], acoustic sensors [14], or image-based techniques [15, 16, 17]. Radar-based methods suffer from limitations related to radar cross-section (RCS), which defines the minimum detectable size of an object. As such, these systems are often ineffective for detecting small UAVs and are more suited to larger aerial targets. RF- and acoustic-based methods, on the other hand, are highly sensitive to environmental noise, often resulting in poor performance when detecting smaller UAVs. With the rapid advancement of deep learning in image processing, image-based object detection has become significantly more feasible and accurate. Using this technology, it is now possible to detect UAVs from image inputs with high precision. Generic object detection models such as YOLO [18], SSD [19], and Faster-RCNN [20] can be trained on UAV datasets to identify and localize drones within images. Compared to other detection methods, image-based approaches are simpler and more cost-effective, as they require only basic camera hardware. This makes them especially advantageous for detecting small- to large-sized UAVs. The primary limitation of image-based detection lies in its range, as it can only identify UAVs within the visible field of view of the camera. To develop deep learning models for UAV detection, appropriate datasets are essential for training, evaluation, and testing. Several datasets have been introduced for UAV object detection tasks [21; 22; 23; 24]. These datasets are typically designed for object detection and provide UAV locations in the form of bounding boxes. Models trained on these datasets consume RGB or infrared (IR) images and attempt to localize UAVs by predicting bounding boxes around them. Consequently, all existing UAV datasets and associated models are designed solely for object detection tasks. While many datasets and detection models exist, they often struggle with small object detection. Some methods have been proposed to improve performance on small objects, but they typically result in increased model size and reduced real-time performance. Furthermore, recent anti-UAV challenges [22] have expanded the focus beyond detection to include UAV tracking to neutralize threats posed by unauthorized UAVs. In this study, we propose a new direction for the anti-UAV task by utilizing semantic segmentation instead of traditional object detection. Semantic segmentation assigns a class label to each pixel in the image, providing superior accuracy for UAV localization. Compared to bounding box methods, semantic segmentation is better suited for detecting small UAVs due to its pixel-level precision. However, to the best of our knowledge, no UAV-specific dataset for semantic segmentation currently exists.