# 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
#### 3.2.4 Adaptive Kernel Convolution (AKConv)  
In the YOLOv7-tiny algorithm, traditional standard convolution plays a crucial role in extracting spatial features. However, it suffers from limitations [28], including a fixed sampling window that prevents it from capturing long-range dependencies [29] and a quadratic growth in parameters as the kernel size increases [29], limiting its adaptability to varying object sizes and shapes. To address these issues, we introduced Adaptive Kernel Convolution (AKConv), a novel convolution method that dynamically adjusts the kernel size and sampling shape based on input feature requirements [30]. This flexibility makes AKConv particularly effective in UAV scenarios, where objects vary significantly in size, shape, and position due to changes in altitude and perspective. This flexible design allows AKConv to accurately match object features and efficiently control computational resource consumption [29], achieving an optimized balance between network performance and overhead.  
As shown in Figure 6, the parameter count of AKConv grows linearly with increasing kernel size, while the parameter count of standard convolution and deformable convolution grows exponentially [12]. This linear growth property allows AKConv to effectively manage computational resource requirements even with larger kernel sizes, making it particularly suitable for UAV platforms with limited resources. Figure 7 illustrates the detailed workflow of AKConv, highlighting its dynamic sampling mechanism. Initially, the input feature map is processed by a preliminary convolution layer to generate offsets (2N, H, W), which define the shifts in sampling points to better capture object features. By adjusting the sampling points, the convolution kernel can adapt to changes in object shapes and scales, thereby enhancing feature representation capabilities. After applying these offsets, the feature map is resampled to align with the adjusted sampling coordinates, ensuring that each convolution operation captures contextually relevant features. Subsequently, the resampled feature map undergoes a processing pipeline that includes reshaping, convolution, normalization, and the SiLU activation function. These steps collectively enhance the discriminative power and robustness of features. The flexible structure of AKConv dynamically adjusts to varying inputs, ensuring effective feature extraction in complex environments.  
The integration of AKConv not only improves detection capabilities in dynamic scenarios but also enables the convolution operation to adjust its kernel size and sampling shape dynamically. This makes AKConv highly effective in detecting small objects, occluded objects, and densely packed targets in complex backgrounds. Furthermore, its linear parameter growth significantly reduces computational and storage requirements, making it particularly well-suited for UAV platforms with constrained hardware resources. By seamlessly integrating with the BiFPN feature fusion module and the Partial_C_Detect detection head, AKConv provides richer feature representations, enhancing multi-scale detection capabilities and boosting the overall performance of the YOLOv7-tiny algorithm in UAV scenarios. Its lightweight and scalable design enables hardware-specific optimizations, ensuring detection accuracy while maintaining strict control over model parameter size and power consumption, making it ideal for UAV applications.