Abstract
In order to improve the recognition, detection and grasping of mechanical parts by mechanical arm of factory automatic assembly line and solve the problems of large detection error and low accuracy in traditional mechanical parts feature extraction algorithm, common mechanical parts were taken as the research target and combined lightweight network in deep learning algorithm as the base model for optimization. CSP-Darknet53 was used to extract the feature. An improved MA-RFB module was added in front of the prediction end, and multi-branch convolution and empty convolution were introduced to strengthen the receptive field. In addition, the neck network was improved, PANet was selected to replace FPN, and attention module CBAM was added to form RC-PANet for multi-scale detection of parts targets. AP reaches 96.47% in the self-made part dataset, and detection speed is 0.00138s per sample. Without losing too much speed, compared with the original YOLOv4-Tiny network, AP improved by 2.80%, and the improved algorithm achieved a balance in speed and precision, which reflected the theoretical and application value of the research.
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References
Qiu, H., Zheng, Q., Msahli, M., et al.: Topological graph convolutional network-based urban traffic flow and density prediction. IEEE Trans. Intell. Transp. Syst. 22(07), 4560–4569 (2020)
Li, Y., Song, Y., Jia, L., et al.: Intelligent fault diagnosis by fusing domain adversarial training and maximum mean discrepancy via ensemble learning. IEEE Trans. Ind. Inform. 17(04), 2833–2841 (2021)
Zhao, J.Q., Du, B.S.: Development of small target detection technology based on deep learning. Electrooptics Optics Control, 1–10 (2022)
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: SSD: Single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2
Huang, J.C., Zhou, J., Ding, L., et al.: Fast detection method of part target based on improved YOLOv3 algorithm. J. Nanjing Inst. Tech. 18(03), 6–11 (2020)
Yu, Y.W., Peng, X., Du, L.Q., et al.: Real-time detection of parts by assembly robot based on deep learning framework. Acta Armamentarii 41(10), 2122–2130 (2020)
Zhong, B.H., Wang, L., Zhong, S.S.: Selective assembly for coordinator parts by rngru based on comprehensive grey relational order model. China Mech. Eng. 32(03), 314–320+356 (2021)
Szegedy, C., Liu, W., Jia, Y., et al.: Going deeper with convolutions. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-9. IEEE Press, USA (2015)
Yang, Z.Y, Wang, J.J, Jin, L.: Human fall detection method based on SE-CNN. Comput. Eng. 1–10 (2022)
Cai, G.Y., Chu, Y.Y.: Visual sentiment analysis based on multi-level features fusion of dual attention. Comput. Eng. 47(09), 227–234 (2021)
Lin, T.Y., Dollár, P., Girshick, R., et al.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125. IEEE, New York (2017)
Liu, S., Qi, L., Qin, H., et al.: Path aggregation network for instance segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 8759–8768. IEEE, Piscataway (2018)
Yun, S., Han, D., Oh, S.J., et al.: Cutmix: regularization strategy to train strong classifiers with localizable features. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. IEEE, New York, 6023–6032 (2019)
Wang, X.P, Wang, X.Q, Lin, Hao.: Review on improvement of typical object detection algorithms in deep learning. Comput. Eng. Appl. 58(06), 42–57(2022)
Acknowledgment
This work is supported in part by the National Natural Science Foundation of China under Grant 51475251, the Natural Science Foundation of Shandong Province under Grant ZR2013FM014 and in part by the Qingdao Municipality Livelihood Plan Pro-ject under Grant 22–3-7-xdny-18-nsh.
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Du, B., Fang, T., Gao, L., Yang, G., Zhao, J. (2022). Recognition of Mechanical Parts Based on Improved YOLOv4-Tiny Algorithm. In: Memmi, G., Yang, B., Kong, L., Zhang, T., Qiu, M. (eds) Knowledge Science, Engineering and Management. KSEM 2022. Lecture Notes in Computer Science(), vol 13368. Springer, Cham. https://doi.org/10.1007/978-3-031-10983-6_51
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DOI: https://doi.org/10.1007/978-3-031-10983-6_51
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