{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T11:06:45Z","timestamp":1790075205410,"version":"4.0.1"},"reference-count":28,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,20]],"date-time":"2026-09-20T00:00:00Z","timestamp":1789862400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Austin Powder"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Borehole detection in textured quarry reconstructions is challenging because shadows, rocks, and fractured ground can resemble visible surface openings. We introduce GeoBoreNet, which augments RGB with four pixel-aligned inputs derived from reconstructed surface height: local residual relief, spatial gradient magnitude, spatial Laplacian response, and mesh support. Plane removal and resolution-adapted local-background subtraction encode surface morphology, and an expanded first convolution incorporates these channels into standard detectors. Experiments evaluate DINO-R50 and YOLO11x across 11 source scenes using scene-disjoint leave-one-scene-out testing, with five paired training seeds for DINO-R50. In pooled cross-fold evaluation, adding geometry increases AP from 0.483 to 0.548 for DINO-R50 and from 0.385 to 0.450 for YOLO11x, an absolute gain of 0.065 for each detector. Component and local-background scale studies further assess the geometry representation. DINO-R50 source-object-unique AP increases from 0.448 to 0.493 at cross-patch NMS IoU 0.4. In evaluated search regions containing zero-object tiles, AP increases from 0.380 to 0.430 for DINO-R50 and from 0.300 to 0.360 for YOLO11x. These results show that local surface morphology complements RGB for two-dimensional borehole detection in the evaluated scenes.<\/jats:p>","DOI":"10.3390\/s26185963","type":"journal-article","created":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T10:21:48Z","timestamp":1790072508000},"page":"5963","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["GeoBoreNet: RGB\u2013Geometry Fusion for Cross-Scene Borehole Detection"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-1189-126X","authenticated-orcid":false,"given":"Xuesong","family":"Liu","sequence":"first","affiliation":[{"name":"Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY 14623, USA"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"given":"Wenbo","family":"Cao","sequence":"additional","affiliation":[{"name":"Independent Researcher, Washington, DC 20005, USA"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"given":"Anke","family":"Xu","sequence":"additional","affiliation":[{"name":"Independent Researcher, New York, NY 10001, USA"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3643-8245","authenticated-orcid":false,"given":"Emmett J.","family":"Ientilucci","sequence":"additional","affiliation":[{"name":"Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY 14623, USA"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Bamford, T., Medinac, F., and Esmaeili, K. (2020). Continuous Monitoring and Improvement of the Blasting Process in Open Pit Mines Using Unmanned Aerial Vehicle Techniques. Remote Sens., 12.","DOI":"10.3390\/rs12172801"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1109\/TASE.2013.2241425","article-title":"Robotic Explosive Charging in Mining and Construction Applications","volume":"11","author":"Bonchis","year":"2014","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Liu, L., Mihankhah, E., Wallace, N.D., Martinez, J., and Hill, A.J. (2025). Blast Hole Seeking and Dipping: Navigation and Perception Framework in a Mine Site Inspection Robot. Robotics, 14.","DOI":"10.3390\/robotics14120173"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"913","DOI":"10.1111\/ecog.02881","article-title":"Cross-Validation Strategies for Data with Temporal, Spatial, Hierarchical, or Phylogenetic Structure","volume":"40","author":"Roberts","year":"2017","journal-title":"Ecography"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Valencia, J., Emami, E., Battulwar, R., Jha, A., Gomez, J.A., Moniri-Morad, A., and Sattarvand, J. (2024). Blasthole Location Detection Using Support Vector Machine and Convolutional Neural Networks on UAV Images and Photogrammetry Models. Electronics, 13.","DOI":"10.3390\/electronics13071291"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Deng, H., Liu, Y., Xu, Q., and Liu, G. (2022). A Semi-Supervised Semantic Segmentation Method for Blast-Hole Detection. Symmetry, 14.","DOI":"10.3390\/sym14040653"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Pan, S., Tian, Z., Qin, Y., Yue, Z., and Yu, T. (2023). Intelligent Blasthole Detection of Roadway Working Face Based on Improved YOLOv7 Network. Appl. Sci., 13.","DOI":"10.3390\/app13116587"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Hu, Q., Xue, J., Yang, D., Zhang, P., and Wang, X. (2026). Deep Learning-Based Binocular Vision for Blast Hole Recognition and Localisation. Int. J. Min. Reclam. Environ., 1\u201324.","DOI":"10.1080\/17480930.2026.2626833"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1007\/978-981-95-0988-1_12","article-title":"Adaptive Contextual Embedding for Robust Far-View Borehole Detection","volume":"Volume 2640","author":"Ma","year":"2025","journal-title":"Generalizing from Limited Resources in the Open World"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"105790","DOI":"10.1016\/j.tust.2024.105790","article-title":"Intelligent Recognition and Parameter Acquisition of Blastholes in Rock Tunnel Based on Improved Faster R-CNN","volume":"149","author":"Zhang","year":"2024","journal-title":"Tunn. Undergr. Space Technol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"28425","DOI":"10.1038\/s41598-024-77034-0","article-title":"An Intelligent Emulsion Explosive Grasping and Filling System Based on YOLO-SimAM-GRCNN","volume":"14","author":"Yi","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ni, J., Zhu, S., Tang, G., Ke, C., and Wang, T. (2024). A Small-Object Detection Model Based on Improved YOLOv8s for UAV Image Scenarios. Remote Sens., 16.","DOI":"10.3390\/rs16132465"},{"key":"ref_13","unstructured":"Zhu, X., Su, W., Lu, L., Li, B., Wang, X., and Dai, J. (2021, January 3\u20137). Deformable DETR: Deformable Transformers for End-to-End Object Detection. Proceedings of the International Conference on Learning Representations (ICLR), Virtual."},{"key":"ref_14","unstructured":"Zhang, H., Li, F., Liu, S., Zhang, L., Su, H., Zhu, J., Ni, L.M., and Shum, H.Y. (2023, January 1\u20135). DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection. Proceedings of the International Conference on Learning Representations (ICLR), Kigali, Rwanda."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1007\/978-3-319-10584-0_23","article-title":"Learning Rich Features from RGB-D Images for Object Detection and Segmentation","volume":"Volume 8695","author":"Fleet","year":"2014","journal-title":"Computer Vision\u2014ECCV 2014"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1002\/arp.374","article-title":"LiDAR-derived Local Relief Models\u2014A new tool for archaeological prospection","volume":"17","author":"Hesse","year":"2010","journal-title":"Archaeol. Prospect."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.isprsjprs.2017.11.011","article-title":"Beyond RGB: Very high resolution urban remote sensing with multimodal deep networks","volume":"140","author":"Audebert","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ophoff, T., Van Beeck, K., and Goedem\u00e9, T. (2019). Exploring RGB+Depth Fusion for Real-Time Object Detection. Sensors, 19.","DOI":"10.3390\/s19040866"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zambanini, S., Loghin, A.M., Pfeifer, N., M\u00e0rmol Soley, E., and Sablatnig, R. (2020). Detection of Parking Cars in Stereo Satellite Images. Remote Sens., 12.","DOI":"10.3390\/rs12132170"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1692","DOI":"10.1109\/TPAMI.2015.2461544","article-title":"ModDrop: Adaptive Multi-Modal Gesture Recognition","volume":"38","author":"Neverova","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"83930","DOI":"10.1109\/ACCESS.2025.3568706","article-title":"Multi-Sensor Model for Earth Observation Robust to Missing Data via Sensor Dropout and Mutual Distillation","volume":"13","author":"Mena","year":"2025","journal-title":"IEEE Access"},{"key":"ref_22","unstructured":"Jocher, G., and Qiu, J. (2026, September 08). Ultralytics YOLO11. Software. Available online: https:\/\/docs.ultralytics.com\/models\/yolo11\/."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Meng, D., Chen, X., Fan, Z., Zeng, G., Li, H., Yuan, Y., Sun, L., and Wang, J. (2021). Conditional DETR for Fast Training Convergence. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), IEEE.","DOI":"10.1109\/ICCV48922.2021.00363"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Cai, Z., and Vasconcelos, N. (2018). Cascade R-CNN: Delving Into High Quality Object Detection. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2018.00644"},{"key":"ref_26","unstructured":"Chen, K., Wang, J., Pang, J., Cao, Y., Xiong, Y., Li, X., Sun, S., Feng, W., Liu, Z., and Xu, J. (2019). MMDetection: Open MMLab Detection Toolbox and Benchmark. arXiv."},{"key":"ref_27","unstructured":"Loshchilov, I., and Hutter, F. (2019, January 6\u20139). Decoupled Weight Decay Regularization. Proceedings of the 7th International Conference on Learning Representations (ICLR 2019), New Orleans, LA, USA."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","article-title":"Microsoft COCO: Common Objects in Context","volume":"Volume 8693","author":"Fleet","year":"2014","journal-title":"Computer Vision\u2013ECCV 2014"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/18\/5963\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T10:35:52Z","timestamp":1790073352000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/18\/5963"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,20]]},"references-count":28,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["s26185963"],"URL":"https:\/\/doi.org\/10.3390\/s26185963","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,20]]}}}