## 2 Methodology
### Introduction of Shared Lightweight Convolutional Heads
Distinct 1 \(\times\) 1 Conv for scales, shared 3 \(\times\) 3 Conv for extraction, then separate shared Conv for classification and regression. To effectively address the challenge of gradient vanishing that may arise during network training, batch normalization techniques are often employed in network architecture design. By transforming the input data to approximate a standard normal distribution, batch normalization enhances the sensitivity of nonlinear functions to input data. However, the effectiveness of batch normalization is significantly impacted by the batch size. Especially in cases of small batches, the application of batch normalization may lead to increased errors in the output results, thereby affecting the model's performance. To overcome the influence of batch size on normalization effects, this paper proposes an innovative approach: replacing the original Batch Normalization (BN) in the SLCD convolutional layer with Group Normalization (GN). GN is a more robust normalization method that maintains stable performance across different batch sizes. By adopting GN, we can avoid excessive influence of batch size selection on normalization effects, ensuring that the model  
Figure 8: Detect layer structure. All convolution operations are separate and independent, without sharing. Figure 9: Shared convolutional structural layer, with introduction of GN into the Conv layers, shared \(3\times 3\) Conv layers as well as the classification and regression Conv layers. performs well in various scenarios. This improvement not only helps to enhance the model's convergence speed and prediction accuracy but also strengthens its robustness and generalization capabilities. GN is a normalization technique utilized in convolutional neural networks. Unlike Batch Normalization (BN), G = groups channels and normalizes within each group, thereby mitigating the impact of batch size variations on model performance. GN computations are independent of batch size, ensuring stability even with small batch sizes, particularly for high-precision images. e calculates the mean and variance within each channel group, effectively reducing noise. The normalization process of GN is implemented as shown in Equation (4). \[GN(x)=\frac{x-\mu}{\sqrt{\theta^{2}+\epsilon}} \tag{4}\]  
where \(x\) is an input feature, \(\mu\) is the mean computed within each group, \(\theta^{2}\) is the variance computed within each group, and \(\epsilon\) is a local minimum. Specifically, if we have a feature \(x\) shaped as [\(N\), \(C\), \(H\), \(W\)], where \(N\) is the batch size, \(C\) is the number of channels, and \(H\) and \(W\) are the spatial dimensions, first, the \(C\) dimension is divided into \(G\) groups with \(C/G\) channels each, and then the features within each are normalized. In the literature [19], GN has been demonstrated to enhance the performance of localization and classification in the detection head as illustrated in the FCOS paper. Data in Table 5 of the literature [19] indicates that removing GN from classification and regression results in a 1% decrease in model accuracy. Therefore, to uphold the detection performance of the detection head, the GN module is integrated into all convolutional layers of the SLCD. The structure of these convolutional layers is depicted in Figure 10. From the figure, it can be seen that the original batch normalization in the Conv structure has been replaced by GN, resulting in a Conv_GN structure. This enhancement not only accelerates the convergence speed of model training but also improves detection accuracy.