Mobile Object Detection Using 2D and 3D Basic Geometric Figures in Colour and Grayscale

In this paper, we use TensorFlow Mobile Lite for Object Detection with datasets of basic geometric figures on iOS mobile devices. Additionally, we trained 4 datasets in 2D and 3D and we compare the accuracy of detection between using colour and grayscale image data. Also, we evaluate the detection rate using 2D and 3D for some kind of normal objects in precision and label output. We used Convolutional Neural Networks (CNN) for build the datasets and OPENCV for convert into grayscale. We value the result relation between flat and volume datasets in way of label and numeric detection, also the affectation of TensorFlow Mobile with this kind of datasets. We make comparisons based on the results of the different experiments object the detection and, in this work, TensorFlow Mobile Lite implementation does not have pseudo boxes and the reason is explained for detection purposes and accuracy adjusted to this kind of experiments.