  Visuotactile sensing technology is becoming more popular in tactile sensing,
but the effectiveness of the existing marker detection localization methods
remains to be further explored. Instead of contour-based blob detection, this
paper presents a learning-based marker localization network for GelStereo
visuotactile sensing called Marknet. Specifically, the Marknet presents a grid
regression architecture to incorporate the distribution of the GelStereo
markers. Furthermore, a marker rationality evaluator (MRE) is modelled to
screen suitable prediction results. The experimental results show that the
Marknet combined with MRE achieves 93.90% precision for irregular markers in
contact areas, which outperforms the traditional contour-based blob detection
method by a large margin of 42.32%. Meanwhile, the proposed learning-based
marker localization method can achieve better real-time performance beyond the
blob detection interface provided by the OpenCV library through GPU
acceleration, which we believe will lead to considerable perceptual sensitivity
gains in various robotic manipulation tasks.
