Research on image recognition and processing of motion targets of warehouse logistics robots

[1] Nasirahmadi, A., Edwards, S. A., & Sturm, B. (2017). Implementation of machine vision for detecting behaviour of cattle and pigs. Livestock ence, 25-38. Search in Google Scholar

[2] Dawood, T., Zhu, Z., & Zayed, T. (2017). Machine vision-based model for spalling detection and quantification in subway networks. Automation in Construction, 81(sep.), 149-160. Search in Google Scholar

[3] Ngo, N. V., Hsu, Q. C., Hsiao, W. L., & Yang, C. J. (2017). Development of a simple three-dimensional machine-vision measurement system for in-process mechanical parts. Advances in Mechanical Engineering, 9(10), 168781401771718. Search in Google Scholar

[4] Zhong, C., Han, J., Borowsky, A., Parvin, B., & Wang, Y. (2017). When machine vision meets histology: a comparative evaluation of model architecture for classification of histology sections. Medical Image Analysis, 35, 530-543. Search in Google Scholar

[5] Li-Ying, C., San-Peng, H. E., Qian, L., Jia-Lin, X., & Dan-Feng, H. (2018). Quantifying muskmelon fruit attributes with a-tep-based model and machine vision measurement. Journal of Integrative Agriculture. Search in Google Scholar

[6] Zhou, L. (2022). The monitoring and early warning system of water biological environment based on machine vision. Mathematical Problems in Engineering, 2022. Search in Google Scholar

[7] Mainzer, K., Killinger, S., Mckenn, R., & Fichtner, W. (2017). Assessment of rooftop photovoltaic potentials at the urban level using publicly available geodata and image recognition techniques. Solar Energy, 155(oct.), 561-573. Search in Google Scholar

[8] Ling, Y., Chen, P., Li, J., Zhang, J., & Chen, K. (2020). Using image recognition and processing technology to measure the gas volume in a miniature water electrolysis device constructed with simple materials. Journal of chemical education. Search in Google Scholar

[9] Serra, P., Cunha, R., Hamel, T., Cabecinhas, D., & Silvestre, C. (2017). Landing of a quadrotor on a moving target using dynamic image-based visual servo control. IEEE Transactions on Robotics, 32(6), 1524-1535. Search in Google Scholar

[10] Kojima, H., Okawara, T., & Trivailo, P. M. (2021). Ellipse detection-based visual servo control for capturing upper-stage rocket body. Acta Astronautica, 182. Search in Google Scholar

[11] Lin, Y., & Xing, K. (2020). Visual servo optimization stabilization of nonholonomic mobile robots based on control lyapunov functions. Measurement and Control, 53(9-10), 1825-1831. Search in Google Scholar

[12] Savchenko, A. V. (2017). Maximum-likelihood approximate nearest neighbor method in real-time image recognition. Pattern Recognition, 61, 459-469. Search in Google Scholar

[13] Song, P., Si, H., Zhou, H., Yuan, R., & Zhang, Z. (2020). Feature extraction and target recognition of moving image sequences. IEEE Access, PP(99), 1-1. Search in Google Scholar

[14] Yun, K., Lim, J., & Choi, J. Y. (2017). Scene conditional background update for moving object detection in a moving camera. Pattern Recognition Letters, 88(Mar.1), 57-63. Search in Google Scholar

[15] Boukhriss, R. R., Fendri, E., & Hammami, M. (2019). Moving object detection under different weather conditions using full-spectrum light sources. Pattern Recognition Letters, 129. Search in Google Scholar

[16] Qureshi, K. N., Kaiwartya, O., Jeon, G., & Piccialli, F. (2022). Neurocomputing for internet of things: object recognition and detection strategy. Neurocomputing(May 7), 485. Search in Google Scholar

[17] He, H. Y. Z. (2017). Moving object recognition using multi-view three-dimensional convolutional neural networks. Neural computing & applications, 28(12). Search in Google Scholar

[18] Yan, Li, Shenglu, Zhou, Qing, & Zhu, et al. (2018). Automated moving object classification in wireless multimedia sensor networks. Environmental Pollution. Search in Google Scholar