YOLOv11-SFF: An Improved Algorithm for Detecting Foreign Objects on Mine Conveyor Belts
DOI:
https://doi.org/10.54097/5c39r677Keywords:
Mine Conveyor Belt, Foreign Object Detection, YOLOv11-SFF, Strip Convolution, Feature Alignment and FusionAbstract
To solve the problem of detecting foreign objects on mine conveyor belts with different backgrounds and variations in target scales, difficulty recognising elongated targets, and missed targets or false negatives for small foreign objects, this paper proposes YOLOv11-SFF, a conveyor belt foreign object detection algorithm based on an improved version of YOLOv11n. YOLOv11-SFF first introduces a C3k2_S module based on direction-aware strip convolutions to enhance the model’s ability to learn the long-term dependence structure of thin targets. Strip convolution performs horizontal and vertical convolution operations. Secondly, there is an FSPPF module that combines the FReLU activation function to augment deep feature representation and aggregate multi-scale contextual information for improved recognition, enabling the model to detect small objects. Finally, the FAFM Feature Alignment and Fusion Module is added in the neck network. Deformable convolutions and content-aware attention are used to achieve cross-scale feature space alignment and adaptive fusion, thus improving the detection accuracy of small, long and low-contrast objects on the conveyor belt. Experimental results demonstrate that, compared to the baseline model, the precision, mAP@50 and mAP@50-95 of YOLOv11-SFF increase by 2.4, 2.6 and 1.9 percentage points respectively, validating the effectiveness of YOLOv11-SFF in the task of detecting foreign objects on mine conveyor belts.
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[1] Gao, R. (2025). Research on foreign object detection in coal mine conveyor belts based on an optimized YOLOv8 framework [Master’s thesis]. Shanxi University. https://doi. org/ 10.27284/d.cnki.gsxiu.2025.001402.
[2] Wang, Y. (2022). Design of a large foreign object detection system for belt conveyors based on machine vision [Master’s thesis]. China University of Mining and Technology. https:// doi. org/10.27623/d.cnki.gzkyu.2022.000183.
[3] Liu, J., & Wang, H. (2025). Research and application of video based recognition technology for safety hazards in coal mine belt conveyors. Coal Mine Machinery, 46, 158–163. https://doi.org/10.13436/j.mkjx.202507043.
[4] Wang, G., Zhao, G., & Ren, H. (2019). Analysis on key technologies of intelligent coal mine and intelligent mining. Journal of China Coal Society, 44, 34–41. https://doi.org/10. 13225/ j. cnki. jccs.2018.5034.
[5] Jin, Z., Shi, L., Geng, Y., & et al. (2026). Research status and prospects of YOLO algorithms for object detection in fully mechanized coal mining faces. Coal Science and Technology, 1–24. https://link.cnki.net/urlid/11.2402. TD.20260513. 1444. 004.
[6] Guo, Z., & Zhang, L. (2024). Foreign object recognition on coal mine conveyor belts under low light conditions using an improved YOLOv8 algorithm. Electronic Measurement Technology, 47, 188–196. https://doi.org/10.19651/ j.cnki. emt. 2416685.
[7] Wang, H., & Han, L. (2025). Foreign object detection on coal mine conveyor belts based on an improved YOLOv11 model. Electronic Design Engineering, 33, 66–70. https://doi.org/10. 14022/ j.issn1674 6236.2025.14.013.
[8] Lin, Y., Zheng, J., & Li, Z. (2025). Small object foreign object detection based on a lightweight UD DETR network. Informatization Research, 51, 71–78.
[9] Cheng, J., Bian, C., Zhang, J., & et al. (2025). Enhanced object detection using parallel pooling attention and multi feature fusion. Modern Electronics Technique, 48, 59–67. https:// doi. org/ 10.16652/j.issn.1004 373x.2025.05.010.
[10] Ma, H., Liu, L., & Gao, K. (2024). Lightweight foreign object detection on coal conveyor belts via multi scale feature fusion. Journal of Safety and Environment, 24, 4617–4629. https://doi. org/10.13637/j.issn.1009 6094.2024.0888.
[11] Yuan, X., Zheng, Z. H., Li, Y., & et al. (2026). Strip R CNN: large strip convolution for remote sensing object detection. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 40, pp. 12259–12267). https://doi.org/10. 1609/aaai. v40i15. 38217. DOI: https://doi.org/10.1609/aaai.v40i15.38217
[12] Wang, S., Wu, M., Wei, X., & et al. (2025). An advanced multi source data fusion method utilizing deep learning techniques for fire detection. Engineering Applications of Artificial Intelligence, 142, 109902. DOI: https://doi.org/10.1016/j.engappai.2024.109902
[13] Zhang, W., Zhang, X., Xu, X., & et al. (2026). MHFNet: multimodal hybrid fusion framework for misaligned SAR–optical ship detection. ISPRS Journal of Photogrammetry and Remote Sensing, 231, 151–166. DOI: https://doi.org/10.1016/j.isprsjprs.2025.10.027
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