MCF-Net: Personnel and machinery detection model for complex downhole drilling environments

Zhupeng Jin and Hongcai Li

PLOS ONE, 2025, vol. 20, issue 5, 1-18

Abstract: Detection of personnel and machinery in the drilling environment of underground coal mines is crucial to the safe production of coal. However, the existing detection models seriously affect the accuracy of the detection models due to the problems of insufficient light and mutual occlusion in the underground. To address these problems, this study proposes a lightweight downhole personnel and machinery detection model (MCF-Net), which aims to solve the above problems while improving the detection accuracy and speed of the model. In this study, YOLOv10 is used as the baseline model, and the PSA module in the backbone network is replaced by designing a lightweight attention mechanism MLBAM, which improves the model’s multi-scale feature extraction capability, enhances the model’s detection performance of mutually occluded objects and reduces the model’s complexity. In the neck network, C2f is reconstructed to get C2f-DualConv based on DualConv, and the group convolution technique is used to extract downhole image features, which can effectively overcome the influence of interference factors such as insufficient illumination in the downhole. Finally, Focaler-CIoU is introduced to reconstruct the IoU loss function, which can accurately locate the downhole objects. In addition, this study conducts experiments on a real downhole borehole dataset, and the results show that compared with the baseline model, MCF-Net improves the accuracy by 0.013 for mAP @ 0.5 and 0.046 for mAP @ 0.5:0.95, reduces the model complexity by 2.27MB for Params and 8.4 for GLOPs, and improves the inference speed by FPS is improved by 11.83 f/s.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0320653 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 20653&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0320653

DOI: 10.1371/journal.pone.0320653

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().