# Research on a UAV-View Object-Detection Method Based on YOLOv7-Tiny
## 2 Related Work
Building on this, YOLOv9 further optimized detection performance by integrating dynamic attention mechanisms and adaptive feature fusion strategies. [PERSON] et al. [18] proposed YOLOv9, which introduced multi-resolution feature aggregation to improve detection performance in highly complex scenes significantly. Experiments showed that YOLOv9 achieved mAP scores of 45.7% and 38.6% on the COCO and VisDrone2019 datasets, significantly outperforming previous YOLO versions. YOLOv10 further optimized the model for lightweight design. [PERSON] et al. [19] developed YOLOv10 with adaptive attention modules and lightweight nested convolution networks, reducing computational costs and improving detection speed. Compared to YOLOv9, YOLOv10 reduced model parameters by 20% while exhibiting excellent robustness in small-object-detection and multi-object tracking applications. This makes it particularly suitable for resource-constrained devices. Despite the significant advancements in detection accuracy and adaptability to diverse scenarios achieved by the YOLO series algorithms, their performance on tiny objects or those within complex backgrounds still needs improvement. To address this issue, [PERSON] et al. [20] proposed an enhanced Feature Pyramid Network (Enhanced FPN), which optimizes the flow of information between feature layers, enhancing the model's ability to recognize and classify small objects. Additionally, this improved version of YOLOv7 introduces depthwise separable convolution, reducing the number of model parameters and further adapting it for real-time UAV video-stream processing. However, these optimizations have not fully resolved detection challenges under extreme conditions, such as low-light environments or fast-moving objects. However, these methods still do not fully address the issues of missed and false detections when identifying small objects. In drone aerial-view object detection, the issue of missed and false detections for small objects is a key technical research area. This is because small objects in images are often constrained by resolution and contrast, making them more likely to be missed or falsely detected in complex backgrounds or when occluded by larger objects. Multi-scale feature fusion techniques are widely used to improve the accuracy of small object detection. These techniques combine feature maps from different network depths, enabling capturing both object details and contextual information. For example, the Feature Pyramid Network (FPN) proposed by [PERSON] [6] effectively enhances the detection performance for small objects. Traditional loss functions may lead to insufficient focus on small objects during training. To address this, [PERSON] et al. [13] developed Focal Loss, which adjusts the focus of the loss function to reduce the influence of large objects on small-object detection, thereby improving the detection rate of small objects. Additionally, data augmentation is an effective method for enhancing model generalization and reducing false detections. Techniques such as image rotation, scaling, and cropping can improve the model's ability to recognize small objects [21]. Moreover, attention mechanisms can help the model focus on important parts of the image, thereby improving the accuracy of small-object detection. The CBAM module proposed by [PERSON] [22] is a practical example, significantly improving detection performance by focusing attention on small objects. Although the aforementioned methods have been tested on several benchmark datasets, such as COCO and VOC, and have shown potential in reducing missed and false detections for small objects, they still face significant challenges in real-world drone aerial applications. Specifically, in drone-captured images, where objects are small, densely distributed, and set against complex backgrounds, existing methods often struggle to distinguish small objects accurately. This is especially true in dynamic real-world scenarios, where the performance of these techniques has not yet reached an ideal level.