###### Abstract  
The widespread growth of drone technology is generating new security paradigms, especially with regard to the unauthorized activities of UAVs in restricted or sensitive areas, as well as illegal and illicit activities or attacks. Among the various UAV detection technologies, vision systems in different spectra are postulated as outstanding technologies due to their peculiarities compared to other technologies. However, drone detection in thermal imaging is a challenging task due to specific factors such as thermal noise, temperature variability, or cluttered environments. This study addresses these challenges through a comparative evaluation of contemporary neural network architectures--specifically, convolutional neural networks (CNNs) and transformer-based models--for UAV detection in infrared imagery. The research focuses on real-world conditions and examines the performance of YOLOv9, GELAN, DETR, and ViTDet in different scenarios of the Anti-UAV Challenge 2023 dataset. The results show that YOLOv9 stands out for its real-time detection speed, while GELAN provides the highest accuracy in varying conditions and DETR performs reliably in thermally complex environments. The study contributes to the advancement of state-of-the-art UAV detection techniques and highlights the need for the further development of specialized models for specific detection scenarios. havior, and even environmental conditions [14; 15]. To address the number of challenges in UAV detection, there are four main approaches: radar-based detection [16; 17; 18], radio frequency-based detection [19; 20], acoustic detection [21; 22], and vision-based detection [23; 24; 25; 26; 27]. Each of these has advantages and disadvantages, which are discussed in reviews such as [PERSON] et al. [14]. Other works such as [28; 29; 30] focus on the advantages of sensor fusion to provide robust systems and to compensate for the shortcomings of some methods with the advantages of others, but each of the mentioned approaches includes one or more branches that are under continuous research. The advances in recent years in Artificial Intelligence (AI) are becoming a transversal field of study for all approaches in detection; however, detection by means of computer vision strategies is arousing great interest in the scientific community with challenges such as the Anti-UAV Challenge 2023 [31; 32; 33]. This is due to the advantages that vision systems offer by having a large amount of information for each of the images and the different bands of the electromagnetic spectrum in which these images can be perceived. For example, the infrared thermal images provided by the previous challenge allow to avoid the difficulties of illumination presented by the visible spectrum in low illumination or adverse environmental conditions, as well as other vulnerabilities presented by the other approaches, as in the case of radar, in the case of small objects or low-altitude flights. Within this context, Artificial Intelligence (AI) and the advances in the last decades in computer vision provide powerful tools for object detection in images. In particular, techniques using convolutional neural networks (CNNs) and/or transformers are continuously demonstrating their potential in the context of object detection in multiple domains. This potential is supported by the ability of AI to process and learn from large volumes of data. Although there are numerous studies in the literature on object detection, segmentation, and tracking systems based on CNNs [34; 35; 36] or transformers [37; 38; 39; 40; 41], drone detection involves certain complexities inherent to this type of target object and the scenarios in which they are found [39; 42; 43]. In recent years, particularly in the analysis of complex scenarios, the focus has shifted towards the use of thermal images to enhance visual detection and object tracking systems [44; 45; 46; 47].