# McAW-YOLO: An Efficient Detection Model for Ceramic Tile Surface Defects
## 3 MCAW-YOLO Network Model
### Light-BiFPN Neck  
In order to improve the utilization of features at different scales, reduce model parameters, and increase detection speed, a lightweight neck network called Light-BiFPN was designed in this section; this network was used for feature fusion and aimed to maintain model accuracy while reducing parameters and increasing detection speed.  
In the feature fusion network of YOLOv5, the scale of the feature maps varied greatly, which could easily result in the loss of details or global information, affecting the model's perception of fine-grained and holistic features. Additionally, multiple transmissions of the feature maps could lead to gradual blurring of the feature information, making it difficult for the model to accurately capture the features and positions of defects. Therefore, to improve the expressive power and accuracy of the model, it was necessary to introduce the semantic information of the original image. This paper referred to the bi-directional feature pyramid network [19] and introduced cross-scale connections to fuse input and output features from the same layer. Based on the structure of the YOLOv5 network, a three-channel BiFPN was constructed. By introducing the original semantic information, more  
Figure 2: MobileViTv3 block architecture.  
features and information could be provided to compensate for the missing information in feature propagation, integrating high-resolution and high-semantic information and improving feature fusion's accuracy and expressive power.  
In order to reduce model parameters, memory consumption, computational burden, hardware costs, and usage thresholds, several techniques were employed. Firstly, in the downsampling step of the network, max pooling was used instead of convolution operations to reduce computational workload. The most salient features in the input region were captured by selecting the maximum value, enhancing feature discriminability. Secondly, cost-effective modules such as ghost shuffle convolution [20] and partial convolution (PConv) [21] were introduced. These modules achieved high detection accuracy while maintaining relatively fewer parameters and computational costs, providing efficient computation and inference speed. The structures of ghost shuffle convolution and partial convolution were shown in Figure 3.  
The GSConv module consisted of Conv, DWConv, concatenate (concat), and shuffle modules. Firstly, the convolution module extracted defect features and reduced the number of channels to reduce computational load. Secondly, depth-wise separable convolution was employed to perform separate convolutions on each channel, reducing the number of parameters. Then, the feature maps obtained from both modules were concatenated along the channel dimension to achieve the desired output channel number. Finally, the features obtained from the two modules were fused through the shuffle operation to enhance feature diversity. The shuffle operation alleviated the problem of weak feature extraction and fusion caused by the limited capabilities of depth-wise separable convolution. On the other hand, the partial convolution divided the input features into two parts along the channel dimension, where one part underwent convolution operations for feature extraction. In contrast, the other remained unchanged to reduce computation. According to the GSConv and PConv modules, we redesigned the C3 module as PGSC3, as shown in Figure 4.  
Figure 3: Structures of GSConv and PConv.