Multimode fusion perception for transparent glass recognition

The purpose of this paper is to present a novel tactile sensor and a visual-tactile recognition framework to reduce the uncertainty of the visual recognition of transparent objects.

A multitask learning model is used to recognize intuitive appearance attributes except texture in the visual mode. Tactile mode adopts a novel vision-based tactile sensor via the level-regional feature extraction network (LRFE-Net) recognition framework to acquire high-resolution texture information and temperature information. Finally, the attribute results of the two modes are integrated based on integration rules.

The recognition accuracy of attributes, such as style, handle, transparency and temperature, is near 100%, and the texture recognition accuracy is 98.75%. The experimental results demonstrate that the proposed framework with a vision-based tactile sensor can improve attribute recognition.

Transparency and visual differences make the texture of transparent glass hard to recognize. Vision-based tactile sensors can improve the texture recognition effect and acquire additional attributes. Integrating visual and tactile information is beneficial to acquiring complete attribute features.