Vision-based multi-size object positioning

Accurate object positioning is critical in many indus- trial manufacturing applications. The execution time and precision of the object positioning task have a significant impact on the overall performance and throughput, especially in cost-sensitive industries such as semiconductor manufacturing. In addition, the object positioning algorithm must adapt to changes in object size, features, and environmental conditions in real-time. While traditional sensors struggle to cope with dynamic conditions, vision-based perception is more adaptable and robust. Vision- based perception can capture and analyze visual information by using cameras and image processing algorithms, providing a robust way to locate objects in dynamic environments. However, classical perception algorithms based on vision cannot handle objects with different characteristics, and modern object detectors that rely on deep neural networks struggle to adapt to image sizes, resulting in unnecessary computations. To address this, this paper proposes an approach for designing branched multi- input deep neural network (DNN) that considers variations in input image sizes to adapt the input branches. In essence the proposed DNN reduces the computation time for images with lower dimensions. To validate the proposed approach IC dataset is created that represents the variations in object sizes as seen in semiconductor manufacturing machines. Depending on the choice of input branches the average inference time is reduced by over 30% without any significant loss in accuracy.

• Vision-based multi-size object positioning
  V. Jain, S. Mohamed, D. Goswami, S. Stuijk.
  In Digital System Design, 26th Euromicro Conference, DSD 23 Proceedings, pages xyz-xyz. Durres, Albania, 6-8 September 2023. IEEE Computer Society Press, Los Alamitos, CA, USA, 20223. (abstract, pdf, doi).