# RML-YOLO: An Insulator Defect Detection Method for UAV Aerial Images
## 2 Materials and Methods
### Convolution Combined with Receptive Field Attention Mechanism  
One of the key reasons for suboptimal performance in object detection models is the limited feature extraction capability of standard convolution operations. In traditional convolutional operations, the same convolution kernel \(K\) is applied to all positions within the receptive field, causing the model to share parameters across spatial locations. This shared parameter approach fails to capture the variations in feature representations due to positional differences, which ultimately restricts the performance of convolutional neural networks. In the context of UAV-based insulator inspection, where objects can vary greatly in size and appearance due to perspective, a more powerful convolution operation is necessary to improve detection performance. To overcome this limitation, this paper introduces RFCAConv [33] as a replacement for standard convolution in the backbone network. RFCAConv integrates the Receptive Field Attention (RFA) mechanism with the channel attention  
Figure 3: LMSHead structure diagram. (CA) module [34], as shown in Figure 4. The RFA component dynamically highlights the most informative regions within the receptive field, strengthening the model's ability to capture spatially sensitive features and alleviating the constraints caused by shared weights in traditional convolutions. This enhancement significantly improves feature learning, especially in complex scenes with cluttered backgrounds. Meanwhile, the CA module embeds positional information into the channel attention mechanism, which further strengthens the representation power of learned features, particularly in mobile networks where feature diversity is crucial. By fusing RFA and CA, RFCAConv efficiently captures contextual and positional dependencies, elevating the network's proficiency in extracting and modeling features from varied spatial locations within images. This advancement ultimately enhances the overall detection performance in challenging scenarios. Given an input feature map of dimensions \((C,H,W)\), the initial processing stage employs grouped convolutions [35], treating each channel independently and expanding them spatially to increase the receptive field.This results in an intermediate feature map of size \((CK^{2},H,W)\), where \(K\) denotes the kernel size. Batch normalization and ReLU activation are subsequently applied to stabilize training and enhance non-linear representation. Next, the Adjust Shapeoperation increases spatial resolution by reshaping the feature map into \((C,KH,KW)\), facilitating finer processing in subsequent operations. To incorporate spatial attention, global average pooling is performed along both width and height dimensions, generating two feature vectors that capture global spatial information. These are concatenated and passed through a shared \(1\times 1\) convolution to produce a feature map of size \((C,KW+KH,1)\), followed by a non-linear activation function. The resulting  
Figure 4: RFCAConv structure diagram. feature map is then split into two parts corresponding to width and height, and Sigmoid activation generates attention weights. These weights modulate the original feature map to emphasize important spatial regions. Finally, a convolution applies these weights, yielding the output feature map. This attention mechanism mitigates parameter sharing issues and enables the model to selectively focus on critical spatial features, improving its representational capability. Assuming that the input feature map has dimensions \((C,H,W)\), the feature maps are initially processed through grouped convolutions [35], where each channel is treated independently. This process expands each channel spatially to increase the receptive field, resulting in an intermediate feature map of size \((CK^{2},H,W)\), where \(K\) represents the kernel size.