FS_YOLOv8: A Deep Learning Network for Ground Fissures Instance Segmentation in UAV Images of the Coal Mining Area

5 citations as recorded by crossref.

1. A UAV-Based Image Dataset and a Novel Morphological Feature Extraction Framework for Identifying Ground Fissures Caused by Coal Mining Activities Z. Xu et al. https://doi.org/10.1109/JSTARS.2026.3683185
2. A Study on the Identification of Cracks in Mine Subsidence Based on YOLOv8n Improvement L. Zhang et al. https://doi.org/10.3390/pr12122716
3. Lightweight precision model for watermelon seed group density estimation and counting H. Yu et al. https://doi.org/10.1016/j.inpa.2025.07.003
4. Enhanced YOLO11n for UAV-Based Surface Crack Detection in Mining Subsidence Areas M. Wang et al. https://doi.org/10.3390/pr14121988
5. Lightweight coal and gangue detection algorithm based on LTC-Yolov8n R. Liu & C. Wei https://doi.org/10.1080/19392699.2025.2480338