# Research on a UAV-View Object-Detection Method Based on YOLOv7-Tiny
## 3 UAV View Object-Detection Method Based on YOLOv7-Tiny  
### YOLOv7-Tiny Algorithm  
The YOLOv7-tiny algorithm, due to its lightweight nature, is particularly well-suited for resource-constrained environments like mobile or low-power devices. The algorithm enhances performance by reducing the model size and the number of parameters while maintaining high accuracy [22]. Its versatility and ease of use allow it to be widely deployed across various platforms, making it highly suitable for UAV operations and monitoring applications. The algorithm combines lightweight design, high performance, and easy deployment, providing an excellent solution for object detection on resource-limited devices. Given that UAV aerial object-detection tasks require flexibility and high resolution but are constrained by limited computational capacity, this paper selects the compact yet fast YOLOv7-tiny as the subject of study. The algorithm comprises four parts: input, backbone, neck, and head, with its detailed structure shown in Figure 1.  
The input module is responsible for receiving raw image data, including preprocessing steps such as scaling and normalization, to ensure that the image data are compatible with the format and size required by the model. The main task of the backbone network is to extract features from the input image, where the CBL layer includes convolution operations, batch normalization (BN), and the LeakyReLU activation function for initial feature extraction. The ELAN module aggregates information from different network layers to refine and enhance the features. The max pooling (MP) layer reduces the spatial dimensions of the feature map, helping to decrease computational costs while extracting more abstract features. The neck network, positioned between the backbone and the prediction head, processes and optimizes the features passed from the backbone. The ELAN module processes feature in the neck, and feature fusion and scale adjustment are achieved through Concat and Upsample operations. The SPPCSP module combines Spatial Pyramid Pooling (SPP) with Cross-Stage Partial (CSP) techniques to enhance feature extraction capabilities and optimize model performance. The head is designed to process high-quality feature maps from the neck network and convert them into actual object-detection results through effective convolution operations. This design ensures that the algorithm delivers high detection accuracy and operates efficiently in resource-constrained environments.  
However, this algorithm has some limitations. It may not be as accurate as the larger YOLOv7 to achieve a lightweight design, particularly in highly complex or challenging en  
Figure 1: The YOLOv7-tiny algorithm framework.  
vironments. Although YOLOv7-tiny is designed for devices with limited computational resources, running such an advanced deep learning algorithm on highly resource-constrained UAV systems may still put pressure on system resources. Therefore, to meet the demands and address the limitations of UAV aerial view object-detection tasks, it is necessary to optimize the YOLOv7-tiny algorithm.