# A Real-Time DAO-YOLO Model for Electric Power Operation Violation Recognition
## 3 DAO-YOLO Model
### DC2f Module  
DAO-YOLO's DC2f component constitutes a cornerstone architectural innovation within the framework. The principal architectural advancement relative to the baseline C2f constitutes the systematic replacement of standard Bottleneck blocks with deformable Bottleneck (DBottleneck) modules, enabling enhanced feature extraction capability for non-regular targets through deformable convolution mechanisms. To optimize parametric efficiency and computational load, we systematically substitute standard convolutional layers within the DC2f architecture with depthwise separable convolution (DSConv) [45] modules. Figure 4a illustrates the overall architecture of DC2f. Firstly, DC2f extracts features through the depthwise separable convolution module. Afterwards, the extracted feature maps are equally divided into two parts along the channel dimension. One part flows into the DBottleneck module for deep feature mining, while the remaining part is retained for subsequent fusion. Subsequently, the feature maps generated by each DBottleneck module are concatenated sequentially along the channel dimension from left to right, as shown in Figure 4a, further enhancing the feature diversity. In the terminal processing stage, depthwise separable convolution is strategically deployed to compress the channel dimensions of the fused feature representations, ensuring dimensional compatibility with downstream network layers through parametric efficiency optimization. Figure 4b demonstrates the network design of DBottleneck. Firstly, the DBottleneck module applies a \(3\times 3\) DConv layer to compress the input feature map by halving its channel dimensions. Secondly, the condensed features are processed through a second \(3\times 3\) DConv layer, which expands the channel depth back to the original size while simultaneously capturing higher-level feature representations. Finally, the input feature  
Figure 4: Architecture of DC2f Module. (**a**) Overall architecture; (**b**) architecture of DBottleneck. Figure 3: Architecture of our DAO-YOLO model. The red dashed lines indicate the innovations of the DAO-YOLO model, where DC2f, AMFE, and OCIoU loss denote the deformable C2f, adaptive multiscale feature enhancement, and optimized bounding box regression loss, respectively. The ’\(\times\)2’ denotes two ConvModules connected in series. map is element-wise added to the output of the second DConv module through a shortcut connection. The central innovation of DBottleneck resides in its DConv module. The Bottleneck module in YOLOv8 employs a standard convolutional block architecture. The fixed receptive fields of standard convolution operations limit spatial adaptability to geometric variability in objects, consequently impairing discriminative capability for shape variations. In contrast, DConv can dynamically predict the sampling point locations for convolution operations and introduce a learnable RoI (region of interest) metric factor (which is proportional to the correlation between the sampling area and the object). This structural innovation significantly improves detection robustness for non-regular geometries, with Figure 5 providing schematic visualization of DConv's adaptive receptive field mechanism. For the input feature map, DConv employs two additional convolutional layers to predict the offsets of the original sampling points along with the RoI scaling factor. Next, the sampling positions of the convolutional kernel are dynamically adjusted according to the predicted offsets, while the RoI scaling factor weights these dynamically sampled points. In the end, the input feature map is convolved with the modified sampling points to produce the refined feature map.