## 1 Introduction  
One of the main duties of the state is security. Detecting criminal activities and preventing the cultivation and sale of prohibited products are among their primary duties. The cultivation of hemp, poppy, and similar products and their detection in prohibited areas are among these duties. For this reason, officers have requested help from information technologies. Object recognition and localization are an important branch of the computing field. High-performance and low-latency object detection algorithms are the main focus of researchers. Real-time object detection with devices such as unmanned aerial vehicles (UAVs) and autonomous vehicles is the aim of our work. For this purpose, extensive research has been carried out on CNN-based networks. The object detection framework was designed to have two main stages, a Faster RCNN [1] and Mask RCNN [2]. Later, it was converted to a single-stage design, YOLO [3]. Over time, it has evolved from an anchor-based to an anchor-free design (e.g., YOLOX [4]), as in the case of YOLOv3 [5] and YOLOv4 [6]. For the object detection task, the most suitable network structure was examined through NAS (Network Architecture Search) [7, 8, 9], and an effort was made to improve the performance of the model by distillation [7, 10, 11, 12]. Single-stage detection models, especially the YOLO series models, have begun to be used in real-time applications due to the balance they provide between speed and accuracy [13]. YOLO is a real-time object detection algorithm. The model divides the image into grids, and each grid tries to recognize and locate objects within itself. It requires very few computational resources. YOLO models basically consist of the following three parts: the backbone, neck, and head. The backbone is an important research topic, and backbone studies [14; 15; 16; 17; 18] have contributed to the balance between speed and precision while increasing efficiency in precision. Recently, researchers have proposed DETR [19], an object detection model using a transformer attention mechanism. These models have increased accuracy against classical object detectors by capturing connections between objects using the attention mechanism, even as there is a long distance between them. Despite the superior performance of transformer-based detectors, they turned out to be quite slow compared to the speed of CNN-based models. Small-scale object detection models based on CNNs, such as YOLOX [4] and YOLOv6-YOLOv8 [20; 21; 22], still provide better results in balancing speed and accuracy. A comprehensive review of attention-based object detection methods in computer vision, their applications, and recent developments are reviewed by [PERSON] et al. [23]. In our work, we focus on real-time object detection models, especially the YOLO series for UAV solutions. Although this study uses datasets that emulate UAV perspectives, actual UAV-captured images will be integrated into future evaluations. The backbone structure in the YOLO architecture is generally flat and consists of several convolutional or fully connected layers (FCL). The neck in the YOLO series generally uses a Feature Pyramid Network (FPN) [24] and its variations to combine multi-level features. These neck modules follow the basic architecture. However, the current approach to information fusion has the following flaw: when transferring information between layers, the traditional FPN structure has problems in transmitting information without losses. This is the main reason for the decrease in accuracy and precision of the YOLO series models. AYOLO was born out of the necessity to find a quick and urgent solution to detect illegal agricultural activities. This study was carried out to meet the need for the real-time detection of small, hidden, and similar objects through aerial observation. The architecture was designed to distinguish the subtle, often elusive characteristics of cannabis plants mixed with legal crops.