Abstract
Reliable personnel detection is critical for the safe deployment of autonomous haulage systems in underground mining, where challenging environmental conditions demand robust real-time perception. Existing research has focused primarily on fine-tuned convolutional detectors, while systematic comparisons with zero-shot vision-language models remain limited. This study presents a cross-paradigm benchmark comparing four zero-shot vision-language models (YOLO-World, Grounding DINO, OWL-ViT, and OWLv2) with four fine-tuned YOLO detectors (YOLOv8s, YOLOv9s, YOLO11s, and YOLO26s) using 31,396 real-world underground coal mine images. Detection performance was evaluated using precision, recall, F1-score, average precision, inference speed, and condition- and target scale-specific recall. The experimental results show that the fine-tuned detectors achieved F1-scores of 0.8449–0.8662 and AP50 values of 0.8791–0.9135, with YOLO26s achieving the strongest overall performance. In comparison, the zero-shot models achieved F1-scores of 0.3186–0.5484 and AP50 values of 0.2484–0.5108, with Grounding DINO performing best among the zero-shot models. Fine-tuned detectors also maintained substantially higher recall for occluded personnel and small apparent targets. These findings demonstrate a substantial performance advantage for domain-specific fine-tuning over the evaluated zero-shot approaches and establish a controlled cross-paradigm benchmark for comparing detection paradigms for underground personnel perception.
Recommended Citation
E. Essien and S. Frimpong, "Benchmarking Zero-Shot Open-Vocabulary And Fine-Tuned Object Detectors For Underground Mine Personnel Detection," Sensors, vol. 26, no. 17, article no. 5646, MDPI, Sep 2026.
The definitive version is available at https://doi.org/10.3390/s26175646
Department(s)
Mining Engineering
Publication Status
Open Access
Keywords and Phrases
autonomous haulage; mine safety and health; object detection; personnel detection; underground mining; vision-language models; YOLO
International Standard Serial Number (ISSN)
1424-8220
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2026 The Authors, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
01 Sep 2026
PubMed ID
42740266
Comments
Centers for Disease Control and Prevention, Grant U60OH012685-01-00