# A Lightweight Barcode Detection Algorithm Based on Deep Learning
## 3 Method
### EfficientViT Block and Multi-Scale Linear Attention  
Deploying advanced object detection models in hardware devices often necessitates considering issues such as model detection accuracy and computational cost. A common practice is to introduce modules that contribute to improving accuracy into lightweight models, which, while enhancing model precision, may also increase the model's size and computational requirements.  
To improve the accuracy of barcode detection models and reduce their computational cost, this study introduces the EfficientViT [20] block (as shown in Figure 3) into the backbone of YOLOv8. The EfficientViT block employs the non-linear function ReLU in its self-attention mechanism and replaces the traditional softmax attention mechanism with a linear attention mechanism. Compared to the backbone of YOLOv8, the improved backbone incorporates a global attention mechanism, achieving higher barcode detection accuracy with fewer parameters and computational requirements.  
Specifically, given an input represented as \(x\in R^{N\times f}\), the generalized form of the attention mechanism can be written as follows:  
\[Attention(Q,K,V)_{i}=\sum_{j=1}^{N}\frac{Sim(Q_{i},K_{j})}{\sum_{j=1}^{N} Sim(Q_{i},K_{j})}V_{j} \tag{1}\]  
Figure 3: EfficientViT block (left) and multi-scale linear attention (right).  
Figure 2: The network structure of the improved model.  
where \(Q=xW_{Q},K=xW_{K},V=xW_{V}\), and \(W_{Q}/W_{K}/W_{V}\in R^{N\times d}(N\gg d)\) are learnable linear projection matrices. \(Attention(Q,K,V)_{i}\) is row \(i\) of the matrix \(Attention(Q,K,V)\) and \(Sim(Q,K)\) is the similar equation.  
In the original softmax attention mechanism [21], \(Sim(Q,K)=exp(QK^{T}/\sqrt{d})\). However, in the EfficientViT block, a ReLU-based linear attention mechanism is adopted as the similarity function. Leveraging the associativity of matrix multiplication, Equation (1) can be progressively rewritten as shown in Equation (2). Compared to the attention mechanism based on the softmax function, the computational complexity of the ReLU-based linear attention mechanism is reduced from \(O(N^{2})\) to \(O(N)\). Additionally, the ReLU function consumes fewer computational resources than the softmax function, making it more hardware-friendly for real-world intelligent logistics centers. Each Feed-Forward Network (FFN) layer in the EfficientViT block is followed by a depthwise convolution, which enhances the ReLU linear attention by leveraging convolution to increase the model's focus on spatial local information. Furthermore, group convolutions are utilized within the block to reduce the total number of operations for information aggregation, further improving the efficiency of barcode feature extraction by the model.  
\[\begin{split} Sim(Q,K)&=ReLU(Q)ReLU(K)^{T}\\ Attention(Q,K,V)_{i}&=\frac{\sum_{j=1}^{N}\left[ ReLU(Q_{i})ReLU(K_{j})^{T}\right]V_{j}}{\sum_{j=1}^{N}\left[ReLU(Q_{i})ReLU(K_{j})^{T} \right]}\\ &=\frac{ReLU(Q_{i})\sum_{j=1}^{N}ReLU(K_{j})^{T}V_{j}}{ReLU(Q_{i })\sum_{j=1}^{N}ReLU(K_{j})^{T}}\end{split} \tag{2}\]  
Given the diversity and complexity of real-world scanning scenarios, input images often contain a mixture of different objects, varying lighting conditions, and barcodes occupying different areas. Traditional single-scale processing windows struggle with such multi-scale input data, potentially leading to the omission of critical information. To significantly enhance the model's efficiency in extracting barcode feature information in complex operational scenarios, we retain the Spatial Pyramid Pooling Fast (SPPF) module at the end of the network backbone. Thanks to the multi-scale spatial container embedded in the SPPF module, the model can more effectively process and integrate spatial feature information of different sizes compared to single-scale sliding window pooling methods. This avoids the imbalance of spatial feature information embedded in the image caused by cropping or distortion operations (as shown in Figure 4).