# Research on a UAV-View Object-Detection Method Based on YOLOv7-Tiny
## 3 UAV View Object-Detection Method Based on YOLOv7-Tiny
### The Improved YOLOv7-tiny Algorithm
#### 3.2.3 Partial_C_Detect Object-Detection Header  
In the YOLOv7-tiny algorithm, feature extraction and fusion are fundamental for effective object detection. As shown in Figure 4, feature maps at three different scales are generated from the input image. Specifically, the input image is downsampled by 8, 16, and 32 factors, producing feature maps of sizes 80 \(\times\) 80, 40 \(\times\) 40, and 20 \(\times\) 20, respectively [26]. These feature maps are passed through the feature fusion module, which extracts and retains multi-level information, enhancing the algorithm's ability to detect objects of various sizes.  
However, the original feature extraction process can result in losing small object features due to multiple convolution and downsampling operations, especially in UAV views. This limitation weakens the algorithm's ability to accurately identify small objects and locate them within complex backgrounds, leading to higher false positive and false negative rates [27]. To address this, we introduce Partial Convolution (PConv) as a key component of the detection head. As depicted in Figure 5, PConv is compared with standard convolution and depthwise/group convolution. While standard convolution applies the convolution kernel to the entire feature map, and depthwise/group convolution divides the input into independent groups, PConv selectively applies convolution to only a subset of input channels, leaving the remaining channels unchanged. This selective processing leverages redundancy in the feature map to reduce computational overhead. PConv drastically lowers floating-point operations (FLOPs) and optimizes parameter usage, making it particularly suitable for resource-constrained UAV platforms. Additionally, PConv focuses computational efforts on the most relevant features, minimizing information loss during processing and ensuring robust feature extraction, even in scenarios with dense or occluded objects.  
The Partial_C_Detect detection head combines PConv with a local perception convolution mechanism to further enhance detection performance. Multi-scale feature maps  
Figure 4: The structure of the feature fusion module.  
Figure 5: Standard convolution (convolution), depthwise/group convolution, and partial convolution.  
generated by the BiFPN network are inputs, and the detection head selectively processes these features to convert them into final detection outputs. PConv's multi-scale feature processing ensures that critical information is retained across different resolutions, reducing the loss of feature details. In scenarios with dense or occluded objects, the detection head prioritizes critical areas, effectively reducing false negatives and positives. Furthermore, the low FLOPs and lightweight design of PConv significantly reduce computational resource requirements, making it particularly suitable for deployment on UAV platforms with limited computational and storage resources.  
With these improvements, the Partial_C_Detect detection head demonstrates outstanding hardware adaptability and detection performance. It operates efficiently on resource-constrained UAV platforms while maintaining high detection accuracy. Whether detecting small objects in urban environments or handling densely packed objects in natural landscapes, the detection head significantly enhances detection capabilities.