## 3 Experiment and Analysis
### Ablation Experiment
Through comparison, it can be found that the improved model not only performs well in fine-grained feature extraction but also has a significant improvement in overall detection performance. In the case of low false alarm rate (e.g., 0.00059), the recall of the model is low, only 0.01778, indicating that the model can detect very limited positive class samples at extremely low false alarm rates. This is usually because the model makes positive predictions when it is extremely certain, which reduces false alarms, but also leaves many positive examples unrecognized. For example, when the false alarm rate is 0.22616, the recall rate increases to 0.22616. When the false alarm rate is 0.65669, the recall rate increases to 0.24671, which indicates that the sensitivity of the model improves with the increase in the false alarm rate and more positive samples can be detected, but more false positives are introduced at the same time. However, the performance of the model is more challenging at very low false alarm rates and the recall decreases rapidly, indicating that in practice, the model's ability to detect the positive class will be significantly limited if the system needs to operate at very low false alarm rates. Therefore, at low and very low false alarm rates, the model exhibits low recall and has difficulty with effectively detecting positive class samples. This performance characteristic is suitable for application scenarios where the cost of false alarms is high and highly accurate detection is required. To improve model performance under these conditions, we may need to incorporate other techniques, such as additional training data, multi-model ensembles, or post-processing steps, to increase recall at very low false alarm rates while maintaining a low false alarm rate. This study deeply explores the impact of the sample imbalance problem on the classification performance of machine learning models, especially when it comes to small object detection or dealing with scarce class data. The results show that the sample imbalance significantly reduces the classification accuracy of the model; especially in the case of insufficient samples in a few categories, the classification error rate is significantly increased. This phenomenon is mainly due to the fact that the model is more inclined to learn the majority category, resulting in insufficient ability to effectively classify the minority category. To alleviate the above problems, increasing the number and diversity of training samples has been proven to be effective. However, this study further analyzes the relationship between the increase in sample size and the improvement of classification performance and finds that the decrease in error rate gradually flattens out as the number of samples increases [32]. This indicates that although expanding the size of the training set is one of the effective means to improve the performance of the model, simply relying on increasing the sample size may not be enough to fully solve the sample imbalance problem. Combining strategies such as data augmentation, resampling techniques, and adjusting the loss function can more effectively improve the classification performance for minority classes. In summary, when dealing with the problem of sample imbalance, especially in small object detection or minority class classification applications that require high accuracy, researchers and practitioners should comprehensively consider the number of samples, class distribution, and the model training strategy to improve the overall performance and generalization ability of the model. Future research should further explore how to optimize the classification performance of the model for each category while maintaining the sample balance so as to promote the development of machine learning techniques in practical applications. Figure 9: Comparison of [EMAIL_ADDRESS] before and after improvement.