# Research on a UAV-View Object-Detection Method Based on YOLOv7-Tiny
## 2 Related Work  
Object-detection technology is one of the core issues in computer vision, playing a crucial role, especially in drone image processing. Early object-detection methods were primarily based on traditional image processing techniques, such as feature-based methods like SIFT [5] and HOG [14]. While these traditional methods performed well in simple scenes, they often struggled with complex image backgrounds and rapidly changing object shapes. With the rise of deep learning, object-detection technology underwent a revolutionary transformation. The introduction of Convolutional Neural Networks (CNNs) significantly improved the performance of object detection. Current object-detection algorithms can be divided into two-stage and one-stage approaches. In two-stage algorithms, R-CNN (Regions with CNN features) and their subsequent variants, such as Fast R-CNN and Faster R-CNN, have played a key role in advancing object-detection technology. [PERSON] [15] and colleagues applied Mask R-CNN to high-resolution drone imagery for object detection and segmentation, particularly optimizing the instance segmentation capabilities to handle multi-scale and dynamically changing objects, effectively improving detection accuracy and segmentation efficiency in complex urban and natural landscapes. [PERSON] [16] and colleagues optimized Faster R-CNN by introducing advanced background updating mechanisms and motion prediction models, solving the problem of accurately tracking small moving objects in drone-captured videos in dynamic environments, thereby significantly enhancing the practicality of Faster R-CNN in drone surveillance applications. Although two-stage object-detection algorithms excel in accuracy, they require substantial computational resources, which is a challenge for resource-limited drone platforms. Additionally, when dealing with rapidly changing drone perspectives, these algorithms may struggle to adapt to rapid object changes due to the inherent characteristics of the algorithm, limiting their effectiveness in specific scenarios. In drone aerial-view object detection, achieving high accuracy is crucial, especially in dynamic and complex environments. Among one-stage object-detection algorithms, YOLO simplifies the detection task into a direct regression problem within a single neural network, allowing it to maintain high accuracy while adapting to rapidly changing scenes. In recent years, YOLOv7 and its lightweight version, YOLOv7-tiny, have demonstrated significant performance improvements, making them highly effective for drone-based detection tasks. The YOLOv7 series has gained widespread adoption due to its ability to balance detection accuracy and computational efficiency, which are particularly important for UAV applications with limited resources. YOLOv7-tiny reduces computational resource consumption while maintaining high accuracy, making it suitable for deployment on resource-constrained drone platforms. The new versions of the YOLO series, such as YOLOv8, YOLOv9, and YOLOv10, integrate transformer architectures and complex feature fusion networks, excelling not only in detection accuracy but also achieving significant improvements in detecting small objects and handling complex scenes. YOLOv8, as a milestone version of the YOLO family, introduced transformer-based self-attention mechanisms and improved feature fusion modules, significantly enhancing its ability to detect small and occluded objects. [PERSON] et al. [17] combined multi-scale feature fusion with attentional mechanisms to enhance YOLOv8's performance in complex scenarios. Compared to YOLOv7, YOLOv8 achieved an approximately 3.6 percentage point increase in mAP on the COCO dataset and demonstrated better real-time performance in various lightweight testing scenarios. However, due to its more complex module design, YOLOv8's computational cost and model parameter size increased significantly, limiting its applicability on resource-constrained platforms like drones.