Object surface roughness/texture recognition using machine vision enables for human-machine haptic interaction - Lin, Zixuan; Zheng, Haowei; Lu, Yue; Zhang, Jiaji; Chai, Guohong; Zuo, Guokun (Frontiers in Computer Science, 2024)
Tactile feedback can effectively improve the controllability of an interactive intelligent robot, and enable users to distinguish the sizes/shapes/compliance of grasped objects. However, it is difficult to recognize object roughness/textures through tactile feedback due to the surface features cannot be acquired with equipped sensors. The purpose of this study is to investigate whether different object roughness/textures can be classified using machine vision and utilized for human-machine haptic interaction. Based on practical application, 2 classes of specialized datasets, the roughness dataset consisted of different spacing/shapes/height distributions of the surface bulges and the texture dataset included 8 types of representative surface textures, were separately established to train the respective classification models. 4 kinds of typical deep learning models (YOLOv5l, SSD300, ResNet18, ResNet34) were employed to verify the identification accuracies of surface features corresponding to different roughness/textures. The human fingers' ability to objects roughness recognition also was quantified through a psychophysical experiment with 3D-printed test objects, as a reference benchmark. The computation results showed that the average roughness recognition accuracies based on SSD300, ResNet18, ResNet34 were higher than 95%, which were superior to those of the human fingers (94% and 91% for 2 and 3 levels of object roughness, respectively). The texture recognition accuracies with all models were higher than 84%. Outcomes indicate that object roughness/textures can be effectively classified using machine vision and exploited for human-machine haptic interaction, providing the feasibility of functional sensory restoration of intelligent robots equipped with visual capture and tactile stimulation devices.