2019
DOI: 10.1109/jsen.2019.2912968
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CNN-Based Methods for Object Recognition With High-Resolution Tactile Sensors

Abstract: Novel high-resolution pressure-sensor arrays allow treating pressure readings as standard images. Computer vision algorithms and methods such as Convolutional Neural Networks (CNN) can be used to identify contact objects. In this paper, a high-resolution tactile sensor has been attached to a robotic endeffector to identify contacted objects. Two CNN-based approaches have been employed to classify pressure images. These methods include a transfer learning approach using a pre-trained CNN on an RGB-images datase… Show more

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Cited by 101 publications
(61 citation statements)
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“…In Alameh et al's research [3], transfer learning was used to classify touch modalities obtained through a small 4 × 4 piezoresistive sensory array, by transforming tensorial data into images and then using different CNN models trained on ImageNet [13]. In Gandarias et al's research [5], they used a light CNN based (only three convolutional layers inside) on AlexNet, to identify 22 objects using their pressure map, collected from a 28 × 50 tactile sensory array. Other works include those in [17][18][19].…”
Section: State-of-the-artmentioning
confidence: 99%
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“…In Alameh et al's research [3], transfer learning was used to classify touch modalities obtained through a small 4 × 4 piezoresistive sensory array, by transforming tensorial data into images and then using different CNN models trained on ImageNet [13]. In Gandarias et al's research [5], they used a light CNN based (only three convolutional layers inside) on AlexNet, to identify 22 objects using their pressure map, collected from a 28 × 50 tactile sensory array. Other works include those in [17][18][19].…”
Section: State-of-the-artmentioning
confidence: 99%
“…Targeting the classification of tactile data, the use of the dataset collected in [5] was considered. Tactile data were collected by a high resolution (1400 pressure taxels) tactile array, which was attached to the 6 DOF robotic arm AUBO Our-i5 [5]. A set of piezoresistive tactile sensors was distributed with a density of 27.6 taxels/cm 2 , forming a matrix of 28 rows by 50 columns.…”
Section: Datasetmentioning
confidence: 99%
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