Minimizing the Effect of Specular Reflection on Object Detection and Pose Estimation of Bin Picking Systems Using Deep Learning

The rapid evolution towards industrial automation has widened the usage of industrial applications, such as robot arm manipulation and bin picking. The performance of these applications relies on object detection and pose estimation through visual data. In fact, the clarity of those data significantly influences the accuracy of object detection and pose estimation. However, a majority of visual data corresponding to metal or glossy surfaces tend to have specular reflections that reduce the accuracy. Hence, this work aims to improve the performance of industrial bin-picking tasks by reducing the effects of specular reflections. This work proposes a deep learning (DL)-based neural network model named SpecToPoseNet to improve object detection and pose estimation accuracy by intelligently removing specular reflections. The proposed work implements a synthetic data generator to train and test the SpecToPoseNet. The conceptual breakthrough of this work is its ability to remove specular reflections from scenarios with multiple objects. With the use of the proposed method, we could reduce the fail rate of object detection to 7%, which is much less compared to specular images (27%), U-Net (20%), and the basic SpecToPoseNet model (11%). Thus, it is claimable that the performance improvements gained are positive influences of the proposed DL-based contexts such as bin-picking.