Segnet unveiled: Robust image segmentation via rigorous K-fold cross-validation analysis

Abstract

Background

In computer vision, image segmentation is crucial with applications ranging from autonomous driving to medical imaging.

Objective

To provide reliable segmentation across varied datasets, this study assesses the performance of an image segmentation model based on SegNet.

Method

Using a five-fold and a K-fold cross-validation method, the SegNet model is thoroughly validated. Intersection over Union (IOU), Dice Coefficient, Precision, Recall, Accuracy, and loss metrics are measured in the study to assess how well the model performs and is optimized throughout training.

Results

The SegNet model consistently performs well throughout the folds, with Dice Coefficient values ranging from 88.32% to 89.8% and IOU scores ranging from 94.53% to 95.05%. The model's dependability is confirmed by metrics like precision, recall, and accuracy, all of which often exceed 90%. Loss values between 0.495 and 0.547 show that training optimized the system effectively.

Conclusion

By enhancing the validation reliability, the K-fold cross-validation method highlights by what means the SegNet model segments objects in images across a range of datasets. These outcomes strengthen the confidence in the model's ability to generalize and highlight its potential for several practical uses in image segmentation.