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A Cascaded Deep Neural Network for Position Estimation of Industrial Robots

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Deep Learning for Unmanned Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 984))

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Abstract

The estimation of an object’s position and orientation from images plays an important role in the field of industrial robots and visual servo, the performance of the vision control system is deeply dependent on the image processing model and algorithm. Before deep learning is widely used in computer vision, the traditional image processing methods are successful in handling the low dimension information of image features, but the traditional image processing methods always fail in complex images with high dimension feature information. In this research chapter, our main contribution is to propose a cascaded convolution network that could obtain high precision pose estimates. Where Single Shot MultiBox Detector (SSD) is utilized to obtain the bounding box of the object to narrow down the recognition range. And a convolutional neural network is utilized to detect the orientation of the object. The method is designed for industrial detection tasks, so the optimized method can run in real-time and extract weak features of sample images. To verify the effect of the detection method based on deep learning in the industrial system, a hand-eye system is built for detecting Radio Remote Unit. A series of experiments have been carried out on the system with the proposed method and the traditional method. In general, the proposed method has advantages in accuracy and recognition rate compared with the traditional algorithm.

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Authors and Affiliations

  1. Harbin Institute of Technology, The Research of Intelligent Control and System, Mechanical Building, 92 ** Da Zhi Street, Nan Gang District, Harbin, China

    Weiyang Lin, Chao Ye, Jiaoju Zhou, **nyang Ren & Mingsi Tong

Authors
  1. Weiyang Lin
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  2. Chao Ye
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  3. Jiaoju Zhou
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  4. **nyang Ren
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  5. Mingsi Tong
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Corresponding author

Correspondence to Weiyang Lin .

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Editors and Affiliations

  1. College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia

    Anis Koubaa

  2. College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia

    Ahmad Taher Azar

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Lin, W., Ye, C., Zhou, J., Ren, X., Tong, M. (2021). A Cascaded Deep Neural Network for Position Estimation of Industrial Robots. In: Koubaa, A., Azar, A.T. (eds) Deep Learning for Unmanned Systems. Studies in Computational Intelligence, vol 984. Springer, Cham. https://doi.org/10.1007/978-3-030-77939-9_5

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