# Research on a UAV-View Object-Detection Method Based on YOLOv7-Tiny  
[PERSON]  
1 School of Computer Science, Xi'an Polytechnic University, Xi'an 710600, China; [EMAIL_ADDRESS].  
environments, such as dense urban areas and natural landscapes, can create occlusion and make small object identification difficult, often leading to missed or false detections. Furthermore, lightweight detection algorithms are critical for drone platforms due to their limited computational resources and power constraints. These requirements necessitate balancing high detection accuracy and computational efficiency [9].  
The development of mainstream object-detection algorithms has significantly advanced the field of computer vision, particularly with the rise of deep learning. Although recent one-stage detection algorithms like YOLOv8, YOLOv9, and YOLOv10 have introduced advanced features such as transformer-based architectures and complex feature fusion networks [10; 11; 12], they still face several limitations. These models have increased computational demands and memory requirements, making them less practical for resource-constrained platforms like drones, which have limited power and processing capabilities [7; 8]. Specifically, the transformer-based mechanisms in YOLOv8 and YOLOv10 significantly enhance detection accuracy but come at the cost of increased computational overhead, which is challenging for real-time deployment on lightweight UAVs [6]. Furthermore, these models still struggle with effectively detecting small objects and handling occlusions in complex backgrounds, often resulting in increased missed or false detections in real-world drone applications [13].  
In this context, YOLOv7-tiny is an optimal choice for lightweight applications [9]. YOLOv7-tiny balances detection accuracy and computational efficiency, making it suitable for deployment on resource-constrained drone platforms where reducing computational resource usage is critical [9]. Moreover, it retains the robust object-detection capabilities of the YOLO family, making it highly applicable to drone-based scenarios [9].  
Although YOLOv7-tiny has certain advantages in drone-view object detection, there is still room for improvement in multi-scale object detection, small object detection, and robustness in dynamic environments [11]. This paper proposes an improved object-detection algorithm based on YOLOv7-tiny for drone aerial views to address these issues. The main contributions of this work are summarized as follows:  
* Solving the problem of multi-scale and occlusion: By introducing the VarifocalLoss function and optimizing the feature fusion network to the BiFPN structure, the detection capability for small objects and objects in densely distributed scenes has been significantly improved. This approach enhances the model's adaptability to object size and occlusion changes, improving its ability to identify objects in complex environments accurately;
* Improvement in detection accuracy: By utilizing the new Partial_C_Detect detection head and introducing Adaptive Kernel Convolution (AKConv) technology, this paper optimizes the recognition and localization of small objects explicitly, while reducing false detection in complex backgrounds. Integrating these techniques enhances the model's accuracy and adjusts the sampling shapes and convolution parameters to accommodate objects of different sizes and shapes;
* Optimization of computational resources and accuracy improvement: Introducing the expandable residual (DWR) attention module significantly enhances the model's ability to represent features when processing dynamically changing and complex backgrounds. By optimizing the use of computational resources, the improved algorithm maintains high detection accuracy while being capable of stable operation in environments with limited resources, making it particularly suitable for handling complex UAV aerial views and ensuring the model's applicability and scalability.