Abstract
Deep learning was used to detect boulders that can damage excavators in opencast mines. Different network architectures were applied, i.e., modern YOLOv5, RetinaNet and Mask-RCNN. Studies were carried out in which the results obtained using a few networks were compared. The abovementioned neural networks were exploited in a framework for detection of oversized boulders on a conveyor belt operating in an opencast coal mine. The method is based on the analysis of a certain number of consecutive frames of the film from an industrial camera. The novelty relies on checking the detection of a boulder within subsequent frames and allowing the skipping of a prescribed small number of neighboring frames with false negative detections. This allows one to make a decision about stopping a conveyor belt after detecting a boulder in consecutive frames even when they are interleaved with frames that contained a boulder missed by a detector due to misleading environmental conditions such as shadows or sand. The method was tested on recordings from an opencast mine in Poland. The proposed method can help prevent the failure of expensive equipment.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abdulla, W.: Mask RCNN for Object Detection and Segmentation. https://github.com/matterport/Mask_RCNN. Accessed 14 Nov 2022
Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: Yolov4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020)
Cao, Y., Chen, K., Loy, C.C., Lin, D.: Prime sample attention in object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11583–11591 (2020)
Cheng, R.: A survey: comparison between convolutional neural network and yolo in image identification. In: Journal of Physics: Conference Series, vol. 1453, p. 012139. IOP Publishing (2020)
Córdova, M., et al.: Litter detection with deep learning: a comparative study. Sensors 22(2), 548 (2022)
Dhillon, B.S.: Mining Equipment Reliability. Springer, London (2008)
Fizyr: Keras-RetinaNet. https://github.com/fizyr/keras-retinanet. Accessed 10 Nov 2022
Fujita, H., Itagaki, M., Ichikawa, K., Hooi, Y.K., Kawano, K., Yamamoto, R.: Fine-tuned pre-trained mask R-CNN models for surface object detection. arXiv preprint arXiv:2010.11464 (2020)
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017)
He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904–1916 (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)
Nepal, U., Eslamiat, H.: Comparing yolov3, yolov4 and yolov5 for autonomous landing spot detection in faulty UAVs. Sensors 22(2), 464 (2022)
Parico, A.I.B., Ahamed, T.: Real time pear fruit detection and counting using yolov4 models and deep sort. Sensors 21(14), 4803 (2021)
Qu, X., Wang, J., Wang, X., Hu, Y., Zeng, T., Tan, T.: Gravelly soil uniformity identification based on the optimized mask r-CNN model. Expert Syst. Appl. 212, 118837 (2023)
Rain Juhl, E.F.: Real-time object detection and classification for ASL alphabet. http://cs231n.stanford.edu/reports/2022/pdfs/147.pdf. Accessed 11 Dec 2022
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)
Roboflow.ai (2023). https://roboflow.com/ Accessed 20 Apr 2023
Sciancalepore, M.: Mask R-CNN for object detection and segmentation (working with tf 2.4.1). https://github.com/masc-it/Mask-RCNN Accessed 14 Nov 2022
Suresh, M., Abhishek, M.: Kidney stone detection using digital image processing techniques. In: 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA), pp. 556–561. IEEE (2021)
Thuan, D.: Evolution of yolo algorithm and yolov5: The state-of-the-art object detention algorithm (2021)
Velikanov, V., Kozyr, A., Dyorina, N.: Engineering implementation of view objectives in mine excavator design. Procedia Engineering 206, 1592–1596 (2017)
Vishmitha, D., Yoshika, K., Sivalakshmi, P., Chowdary, V., Shanthi, K., Yamini, M., et al.: Kidney stone detection using deep learning and transfer learning. In: 2022 4th International Conference on Inventive Research in Computing Applications (ICIRCA), pp. 987–992. IEEE (2022)
Wang, C.Y., Liao, H.Y.M., Wu, Y.H., Chen, P.Y., Hsieh, J.W., Yeh, I.H.: CSPNET: a new backbone that can enhance learning capability of CNN. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 390–391 (2020)
Zhang, H.: Image processing for the oil sands mining industry [in the spotlight]. IEEE Signal Process. Mag. 25(6), 200–198 (2008)
Acknowledgments
The authors would like to thank Produs S.A. and The Bełchatów coal mine for their cooperation and research data and to members of the CyberTech circle.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Szymkowiak, J., Bazan, M., Halawa, K., Janiczek, T. (2023). Detection of Objects Dangerous for the Operation of Mining Machines. In: Mikyška, J., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2023. ICCS 2023. Lecture Notes in Computer Science, vol 10476. Springer, Cham. https://doi.org/10.1007/978-3-031-36027-5_10
Download citation
DOI: https://doi.org/10.1007/978-3-031-36027-5_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-36026-8
Online ISBN: 978-3-031-36027-5
eBook Packages: Computer ScienceComputer Science (R0)