Research on Intelligent Supervision System of Ore Pass

Yıl 2023, Cilt: 62 Sayı: 4, 167 - 174, 12.02.2024

Baoshun Liu , Yanyu Song , Yongjing Ye , Zijing Zhang

https://doi.org/10.30797/madencilik.1349081

Öz

To prevent safety accidents caused by mining vehicles and personnel entering the operation area by mistakes, it is necessary to reduce the risk of the ore pass. However, the underground space of the mine is narrow, and factors such as dust and noise during the unloading process endanger the health of the personnel on duty in the ore pass. As such, the target detection technology based on deep learning is introduced into the underground monitoring system. The underground surveillance video samples are collected to establish a dataset for Yolov3 algorithm to identify minecarts. Through optimizing the Yolov3 model training process and algorithm, and using the dual-camera collaborative discrimination method, the influence of brightness on the recognition results when the loaders or trucks lights are turned on can be overcome. Four types of minecarts can be accurately identified from the underground surveillance video. On the basis of mining car recognition, an intelligent access control system for mine shafts based on Jetson Nano’s embedded development is developed. The on-site operation results show that the average accuracy of target vehicle recognition is within the range of 95%-100%. The system continuously recognizes the mine car 5 times from the detection program and sends the opening and closing command to complete a 90 ° rotation, which takes only 3 seconds，effectively meeting the needs of the mine for ore pass control.

Anahtar Kelimeler

Yolov3, ore pass, smart access control, object detection, minecart recognition

Kaynakça

• Baccouche, A., Garcia-Zapirain, B., Zheng, Y., Elmaghraby, A.S. 2022. Early detection and classification of abnormality in prior mammograms using image-to-image translation and YOLO techniques. Computer Methods and Programs in Biomedicine, 221, 106884.https://doi. org/10.1016/j.cmpb.2022.106884
• Girshick, R., Donahue, J., Darrell, T., Malik, J. 2014. Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 580-587.
• Guo, X., Li, Y. 2011. Underground Personnel Positioning System Based on ZigBee. 2011 Fourth International Symposium on Computational Intelligence and Design, Hangzhou, China, IEEE, 1, 298-300. DOI: 10.1109/ ISCID.2011.82
• Imam, M., Baïna, K., Tabii, Y., Ressami, E.M., Adlaoui, Y., Benzakour, I., Abdelwahed, E.H. 2023. The Future of Mine Safety: A Comprehensive Review of Anti-Collision Systems Based on Computer Vision in Underground Mines. Sensors (Basel, Switzerland), 23(9), 4294. https://doi. org/10.3390/s23094294
• Kang, L., Lu, Z., Meng, L., Gao, Z. 2024. YOLO-FA: Type-1 fuzzy attention based YOLO detector for vehicle detection. Expert Systems with Applications, 237(Part B), 121209.https://doi.org/10.1016/j.eswa.2023.121209
• Kumar, A., Kalia, A., Kalia, A., 2022. ETL-YOLO v4: A face mask detection algorithm in era of COVID-19 pandemic. OPTIK, 259, 169051.https:// doi.org/10.1016/j.ijleo.2022.169051
• Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C. Y., & Berg, A. C. 2016. Ssd: single shot multi box detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds) Computer Vision – ECCV 2016. ECCV 2016. Lecture Notes in Computer Science, Springer, Cham, 9905, 21-37. https://doi. org/10.1007/978-3-319-46448-0_2
• Madahana, M.C.I., Nyandoro, O.T.C., Ekoru, J.E.D. 2020. Intelligent comprehensive Occupational health monitoring system for mine workers. IFAC-PapersOnLine, 53(2), 16494-16499. https://doi.org/10.1016/j. ifacol.2020.12.751
• Redmon, J., Farhadi, A. 2016. YOLO9000: Better, faster, stronger. arXiv preprint arXiv:1612.08242. https://doi.org/10.48550/arXiv.1612.08242 Redmon, J., Farhadi, A. 2018. YOLOv3:an incremental improvement. arXiv preprint arXiv:1804.02767. https://doi.org/10.48550/arXiv. 1804.02767
• Ren, S.Q, He, K., Girshick, R., Sun, J. 2017. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Translations on Pattern Analysis and Machine Intelligence, 39(6), 1137-1149. DOI: 10.1109/TPAMI.2016.2577031
• Tian,Y., Wang, S., Li, E., Yang, G., Liang, Z., Tan, M. 2023. MD-YOLO: Multiscale Dense YOLO for small target pest detection. Computers and Electronics in Agriculture, 213, 108233.https://doi.org/10.1016/j.compag. 2023.108233 Yang,C.Y., Li, C., Su, J.C., Wang, X.Q., He, Y.R. 2016. Research on video-based system of activity recognition for coal mine safety surveillance. Coal Engineering, 48(4), 111-113+117.