A Lightweight Rock Core Image Instance Segmentation Method Based on MCE-YOLO11n-seg

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

In hydraulic engineering, manual rock core logging and rock quality designation (RQD) calculation exist the problems of substantial workloads, low efficiency, and high subjectivity. To address these issues, a lightweight instance segmentation model, termed MCE-YOLO11n-seg, was proposed for the automatic logging of rock cores in core box images and automated RQD calculation. Base on YOLO11n-seg, MobileNetV4 was adopted to replace the original backbone, thereby reducing computational complexity and the number of parameters. Additionally, a content-aware reassembly of features (CARAFE) module was introduced to optimize multi-scale feature fusion. Furthermore, an efficient enhanced segmentation head (EES-Head) was developed to sharpen the representation of rock core instance boundaries by utilizing an edge branch and an edge-aware feature modulation mechanism. The results show that MobileNetV4 enhances the lightweight of the model, and CARAFE and EES-Head effectively improve multi-scale feature fusion and boundary segmentation. MCE-YOLO11n-seg achieved 1.89 M parameters, 5.88 G FLOPs, and an inference speed of 89.81 FPS. The proposed model yielded a Precision of 98.18%, a Recall of 98.00%, and mask mAP@0.5:0.95 of 88.03%. Compared with YOLO11n-seg, it reduced parameters and FLOPs by 33.47% and 38.61%, respectively, while increasing the mask mAP@0.5:0.95 by 0.52 percentage points. Practical validation across 23 core box images demonstrated a mean relative error of merely 1.71% and a median relative error of 1.68% between the automated RQD calculations and manual measurements. MCE-YOLO11n-seg achieves a balance between lightweight design and high segmentation accuracy, offering effective support for automated RQD calculation and intelligent core logging.