Texture Detection with Feature Extraction on Embedded FPGA - Dataset

Citation Author(s):
University of Malaga
Submitted by:
Raul Lora-Rivera
Last updated:
Fri, 04/21/2023 - 04:21
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A feature extraction algorithm for texture detection oriented to its implementation on embedded electronics based on a Field-Programmable Gate Array (FPGA) is proposed in this paper.
Local pre-processing with smart tactile sensors can help to improve dexterity in artificial hands. Simplicity is the goal in order to achieve a hardware-friendly strategy that can be replicated and integrated with another circuitry. This is interesting, considering that tactile sensors are arrays and FPGAs are capable of parallel execution. The proposal was tested with a custom smart tactile sensor mounted on a Cartesian robot to explore different textures. A comparison with a common feature extraction approach based on the fast Fourier Transform (FFT)
computation was also made. In addition, the whole procedure is implemented on a System on Chip (SoC) with the feature extraction on the embedded FPGA and a k-means classifier on an ARM core. The proposed algorithm obtains the spatial frequency components of the tactile signal but not their power. Therefore, some information is lost with respect to that provided by the FFT. Nevertheless, an 89.17% accuracy of the proposed algorithm is obtained versus 91.4% with the FFT when twelve different textures are considered, including complex and fabric textures. There is a noticeable saving in power and hardware resources. In addition, since the size of the feature vector is much smaller, data traffic and memory usage is much lower, and the classifier can be simpler.


In this document there are attached twelve .csv files. For each file, there are 200 raws with 2048 values. These are the data obtained from the tactile sensor in this work (see the paper document for more information).
Each raw is an iteration of the 200 iterations performed for every texture in this work (see Section III -> Materials and Methods, Subsection D -> Data Gathering Procedure).
There is also an image with the twelve textures and their corresponding label, so it is easy to identify them (see also Section III -> Materials and Methods, Subsection C -> Texture Samples).