Optimizing Deep Learning Models to Address the Long Tail Problem and its Application in Semantic Segmentation

In the semantic segmentation task, the long-tail problem causes the recognition performance of minority categories to be significantly lower than that of majority categories, affecting the overall effect of the model. This paper aims to optimize the FPN (Feature Pyramid Networks) model to better deal with the long-tail problem and improve the accuracy of semantic segmentation. First, by analyzing the category distribution of the dataset and identifying the severity of the long-tail phenomenon, a semantic segmentation dataset containing multiple categories is constructed. Secondly, for minority categories, data enhancement technology is used and resampling is performed to ensure that the model could be exposed to more minority category samples during training. A focus loss function is then integrated to prioritize hard-to-segment regions and underrepresented categories. Experimental results on standard benchmarks show that the proposed FPN model achieves a Dice coefficient of 88.1% and an F1-Score of 86.4%, significantly outperforming other models such as U-Net (Dice: 85.6%, F1-Score: 83.9%), DeepLab v3 (Dice: 87.4%, F1-Score: 85.6%), and DeepLab v1 (Dice: 84.3%, F1-Score: 82.8%). Notably, the minority class F1-Score for FPN reaches 72.1%, demonstrating its superior ability to tackle the long-tail problem. The results indicate that targeted methods, including dataset augmentation and loss function adjustment, can significantly enhance deep learning model performance, offering new insights for improving semantic segmentation tasks.