Comparative Analysis of YOLOv7 with Modified Mosaic Augmentation Against YOLOv8-11 for Object Detection in Unbalanced Datasets

This study evaluates the performance of YOLOv7 with modified mosaic augmentation, which resizes images without cutting, against newer YOLO versions (YOLOv8, YOLOv9, YOLOv10, and YOLOv11) when trained with an unbalanced dataset in the context of sewage inspection. The modified mosaic augmentation helps preserve small, underrepresented objects in unbalanced datasets which are often cut off with original mosaic augmentation. Results show that YOLOv7 with modified mosaic augmentation outperforms YOLOv7 and YOLOv8 in precision and recall metrics, while YOLOv9 slightly surpasses it. However, YOLOv10 and YOLOv11 offer the best overall performance, in terms of both precision and recall. These findings show the importance of model selection based on application-specific needs and highlight the potential of modified mosaic augmentation for handling class imbalances. Future research should validate these findings across different datasets.