# DGBL-YOLOv8s: An Enhanced Object Detection Model for Unmanned Aerial Vehicle Imagery
## 3 Methods
### Introduction of DGBL-YOLOv8 Object Detection Algorithm
#### 3.1.1 The C2f_DWR Module  
Images taken by UAVs are frequently small-scale, taken in high quantities and at long distances. These characteristics challenge the C2f module in the YOLOv8s model, limiting its ability to extract adequate effective information from multi-scale feature maps and cutting down detection accuracy. To tackle this issue, this study introduces the C2f_DWR module as a replacement for the C2f module. This new module strengthens feature extraction and enhances the integration of multi-scale features. The design of the C2f_DWR module is depicted in Figure 2.  
Figure 1: Architecture of the DGBL-YOLOv8. The DWR module employs a two-step approach to capture multi-scale contextual information. In the first step, Region Residualization generates a series of compact feature maps in the form of regions of varying sizes by utilizing a combination of \(3\times 3\) convolutions, batch normalization (BN) layers, and ReLU layers. This process transforms the input features into representations with distinct regional characteristics, thereby simplifying the subsequent extraction of multi-scale information. In the second step, Semantic Residualization applies morphological filtering to features of different region sizes using multi-rate dilated depth-wise convolutions. The multi-rate dilated depth-wise convolutions consist of three components: multi-rate, depth-wise separable [21], and dilated convolutions [22]. The initial phase of this stage utilizes dilated convolutions with diverse dilation rates to enhance the receptive field and capture richer contextual information. Following this, depth-wise separable convolutions are implemented to improve the model's feature extraction capability by increasing the channel dimension, all while minimizing the number of parameters and the computational cost. The structural design of the DWR module is illustrated in Figure 3.  
In standard convolution operations, the receptive field size depends on the kernel size and network depth. To obtain a larger receptive field, a model typically increases network depth or expands the convolution kernel size. However, this method results in elevated computational demands and a larger parameter volume, while substantially diminishing the spatial resolution of feature maps, ultimately compromising the model's capacity to retain fine-grained image details and edge information. To address this issue, the C2f_DWR module employs multi-rate dilated convolution, which amplifies the receptive field without increasing computational complexity or reducing image resolution. This allows the model to capture an immense range of contextual information efficiently. Figure 3: Architecture of the DWR module. Figure 2: C2f_DWR. Specifically, dilated convolution enlarges the receptive field by inserting gaps (dilations) between kernel elements. The number of inserted gaps is determined by the dilation rate, which is calculated using the formula given in Equation (1):  
\[a^{\prime}=a+(a-1)\times(b-1) \tag{1}\]  
In the equation, a represents a convolution of size \(a\times a\), and \(a^{\prime}\) denotes a convolution of size \(a^{\prime}\times a^{\prime}\). Parameter \(b\) refers to the dilation rate, which is set to \(b\) = 1, 3, 5 in this study, indicating the use of three different dilation rates for dilated convolution. By applying dilated convolution, the receptive field of the convolution operation is effectively expanded, enabling the model to capture a wider range of contextual information. Additionally, the C2f_DWR module employs depth-wise separable convolution to reduce computational complexity and parameter count while enhancing the model's feature extraction capability. This convolution module consists of depth-wise convolution and point-wise convolution, which are responsible for extracting spatial and channel features, respectively.