122
Views
0
CrossRef citations to date
0
Altmetric
Research Articles

A visual detection method for conveyor belt misalignment based on the improved YOLACT network

, , ORCID Icon, , &

References

  • Bai, W., J. Zhao, C. Dai, H. Zhang, L. Zhao, Z. Ji, and I. Ganchev. 2023. Two novel models for traffic sign detection based on YOLOv5s. Axioms 12 (2):160. doi: 10.3390/axioms12020160.
  • Bolya, D., C. Zhou, F. Xiao, and Y. J. Lee. 2019. YOLACT: Real-time instance segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 9157–66. Seoul (South Korea): IEEE.
  • Bolya, D., C. Zhou, F. Xiao, and Y. J. Lee. 2022. YOLACT++ Better real-time instance segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44 (2):1108–21. doi: 10.1109/TPAMI.2020.3014297.
  • Bortnowski, P., W. Kawalec, R. Król, and M. Ozdoba. 2022. Types and causes of damage to the conveyor belt – Review, classification and mutual relations. Engineering Failure Analysis 140:106520. doi: 10.1016/j.engfailanal.2022.106520.
  • Chamorro, J., L. Vallejo, C. Maynard, S. Guevara, J. A. Solorio, N. Soto, K. V. Singh, U. Bhate, R. K. G.v.v, J. Garcia, et al. 2022. Health monitoring of a conveyor belt system using machine vision and real-time sensor data. CIRP Journal of Manufacturing Science and Technology 38:38–50. doi: 10.1016/j.cirpj.2022.03.013.
  • Chen, B., Y. Liu, and K. Sun. 2021. Research on object detection method based on FF-YOLO for complex scenes. IEEE Access 9:127950–60. doi: 10.1109/ACCESS.2021.3108398.
  • Chen, H., K. Sun, Z. Tian, C. Shen, Y. Huang, and Y. Yan. 2020. BlendMask: Top-down meets bottom-up for instance segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR), 8573–81. Seattle (WA): IEEE.
  • Garg, P., and T. Jain. 2017. A comparative study on histogram equalization and cumulative histogram equalization. International Journal of New Technology and Research 3 (9):41–3.
  • Girshick, R., J. Donahue, T. Darrell, and J. Malik. 2014. Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 580–7. Columbus (OH): IEEE.
  • He, K., G. Gkioxari, P. Dollar, and R. Girshick. 2017. Mask R-CNN. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2980–8. Venice (Italy): IEEE.
  • Huang, Z., L. Huang, Y. Gong, C. Huang, and X. Wang. 2019. Mask scoring R-CNN. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 6409–18. Long Beach (CA): IEEE.
  • Jha, S. B., and R. F. Babiceanu. 2023. Deep CNN-based visual defect detection: Survey of current literature. Computers in Industry 148:103911. doi: 10.1016/j.compind.2023.103911.
  • Li, K., G. Wan, G. Cheng, L. Meng, and J. Han. 2020. Object detection in optical remote sensing images: A survey and a new benchmark. ISPRS Journal of Photogrammetry and Remote Sensing 159:296–307. doi: 10.1016/j.isprsjprs.2019.11.023.
  • Li, Y., S. Li, H. Du, L. Chen, D. Zhang, and Y. Li. 2020. YOLO-ACN: Focusing on small target and occluded object detection. IEEE Access 8:227288–303. doi: 10.1109/ACCESS.2020.3046515.
  • Liu, Y., C. Miao, X. Li, and G. Xu. 2021. Research on deviation detection of belt conveyor based on inspection robot and deep learning. Complexity 2021:3734560.
  • Lopez-Montiel, M., U. Orozco-Rosas, M. Sanchez-Adame, K. Picos, and O. H. M. Ross. 2021. Evaluation method of deep learning-based embedded systems for traffic sign detection. IEEE Access 9:101217–38. doi: 10.1109/ACCESS.2021.3097969.
  • Mahaur, B., and K. K. Mishra. 2023. Small-object detection based on YOLOv5 in autonomous driving systems. Pattern Recognition Letters 168:115–22. doi: 10.1016/j.patrec.2023.03.009.
  • Maksimovic, V., M. Petrovic, D. Savic, B. Jaksic, and P. Spalevic. 2021. New approach of estimating edge detection threshold and application of adaptive detector depending on image complexity. Optik 238:166476. doi: 10.1016/j.ijleo.2021.166476.
  • Meng, T., and W. Zhang. 2022. Fast video object segmentation via dynamic YOLACT. In ICASSP 2022–2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2400–2404. Singapore (Singapore): IEEE. doi: 10.1109/ICASSP43922.2022.9747260.
  • Redmon, J., and A. Farhadi. 2018. Yolov3: An incremental improvement, arXiv preprint arXiv:1804.02767.
  • Redmon, J., S. Divvala, R. Girshick, and A. Farhadi. 2016. You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 779–88. Las Vegas (NV): IEEE.
  • Shang, Z., W. Li, M. Gao, X. Liu, and Y. Yu. 2021. An intelligent fault diagnosis method of multi-scale deep feature fusion based on information entropy. Chinese Journal of Mechanical Engineering 34 (1):58. doi: 10.1186/s10033-021-00580-5.
  • Shao, F., X. Wang, F. Meng, J. Zhu, D. Wang, and J. Dai. 2019. Improved faster R-CNN traffic sign detection based on a second region of interest and highly possible regions proposal network. Sensors 19 (10):2288. doi: 10.3390/s19102288.
  • Shen, J., C. Wheeler, J. O'Shea, and D. Ilic. 2018. Investigation of the dynamic deflection of conveyor belts via experimental and modelling methods. Measurement 127:210–20. doi: 10.1016/j.measurement.2018.05.091.
  • Shi, T., W. Zhu, and Y. Su. 2023. Improved light-weight target detection method based on YOLOv5. IEEE Access11:38604–13. doi: 10.1109/ACCESS.2023.3267965.
  • Sivkov, S., L. Novikov, G. Romanova, A. Romanova, D. Vaganov, M. Valitov, and S. Vasiliev. 2020. The algorithm development for operation of a computer vision system via the OpenCV library. Procedia Computer Science 169:662–7. doi: 10.1016/j.procs.2020.02.193.
  • Tian, Z., C. Shen, and H. Chen. 2020. Conditional convolutions for instance segmentation. In Computer Vision–ECCV 2020: 16th European Conference, 282–98. Glasgow (UK): Springer, Cham.
  • Wang, G., B. Zhang, H. Wang, L. Xu, Y. Li, and Z. Liu. 2022. Detection of the drivable area on high-speed road via YOLACT. Signal, Image and Video Processing 16 (6):1623–30. doi: 10.1007/s11760-021-02117-8.
  • Wang, G., L. Zhang, H. Sun, and C. Zhu. 2021. Longitudinal tear detection of conveyor belt under uneven light based on Haar-AdaBoost and Cascade algorithm. Measurement 168:108341. doi: 10.1016/j.measurement.2020.108341.
  • Wang, L., X. Gu, Z. Liu, W. Wu, and D. Wang. 2022. Automatic detection of asphalt pavement thickness: A method combining GPR images and improved Canny algorithm. Measurement 196:111248. doi: 10.1016/j.measurement.2022.111248.
  • Xie, S., R. Girshick, P. Dollar, Z. Tu, and K. He. 2017. Aggregated residual transformations for deep neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 5987–95. Honolulu (HI): IEEE.
  • Xu, C., X. Zeng, R. Zhang, and K. Wang. 2021. Detection method of edge position of belt conveyor based on complex environment. In 2021 4th International Conference on Robotics, Control and Automation Engineering (RCAE), 417–22. Wuhan (China): IEEE. doi: 10.1109/RCAE53607.2021.9638894.
  • Ying, H., Z. Huang, S. Liu, T. Shao, and K. Zhou. 2019. Embedmask: Embedding coupling for one-stage instance segmentation, arXiv preprint arXiv:1912.01954.
  • Zeng, C., J. Zheng, and J. Li. 2019. Real-time conveyor belt deviation detection algorithm based on multi-scale feature fusion network. Algorithms 12 (10):205. doi: 10.3390/a12100205.
  • Zeng, J., H. Ouyang, M. Liu, L. U. Leng, and X. Fu. 2022. Multi-scale YOLACT for instance segmentation. Journal of King Saud University - Computer and Information Sciences 34 (10):9419–27. doi: 10.1016/j.jksuci.2022.09.019.
  • Zhang, C., S. Chen, L. Zhao, X. Li, and X. Ma. 2021. FPGA-based linear detection algorithm of an underground inspection robot. Algorithms 14 (10):284. doi: 10.3390/a14100284.
  • Zhang, M., H. Shi, Y. Zhang, Y. Yu, and M. Zhou. 2021. Deep learning-based damage detection of mining conveyor belt. Measurement 175:109130. doi: 10.1016/j.measurement.2021.109130.
  • Zhang, M., K. Jiang, Y. Cao, M. Li, N. Hao, and Y. Zhang. 2022. A deep learning-based method for deviation status detection in intelligent conveyor belt system. Journal of Cleaner Production 363:132575. doi: 10.1016/j.jclepro.2022.132575.
  • Zhang, Z., S. Huang, X. Liu, B. Zhang, and D. Dong. 2022. Adversarial attacks on YOLACT instance segmentation. Computers & Security 116:102682. doi: 10.1016/j.cose.2022.102682.
  • Zhao, Z., X. Tong, Y. Sun, D. Bai, X. Liu, G. Zhao, H. Fan, J. Li, C. Zou, and B. Chen. 2022. Large scale instance segmentation of outdoor environment based on improved YOLACT. Concurrency and Computation: Practice and Experience 34 (28):e7370. doi: 10.1002/cpe.7370.
  • Zhu, A., G. Hua, and Y. Wang. 2011. The research on the detection method of belt deviation by video in coal mine. In 2011 International Conference on Mechatronic Science, Electric Engineering and Computer (MEC), 430–3. Jilin (China): IEEE.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.