Algorithm of detection, classification and gripping of occluded objects by CNN techniques and Haar classifiers

Paula Useche, Robinson Jimenez-Moreno, Javier Martinez Baquero


The following paper presents the development of an algorithm, in charge of detecting, classifying and grabbing occluded objects, using artificial intelligence techniques, machine vision for the recognition of the environment, an anthropomorphic manipulator for the manipulation of the elements and a virtual system designed to verify the operation of the algorithm prior to its physical application. 5 types of tools were used for their detection and classification, where the user selects one of them, so that the program searches for it in the work environment and delivers it in a specific area, overcoming difficulties such as occlusions of up to 70%. These tools were classified using two CNN (Convolutional Neural Network) type networks, a Fast R-CNN (Fast Region-Based CNN) for the detection and classification of occlusions, and a DAG-CNN (Directed Acyclic Graph - CNN) for the classification tools. Furthermor, a Haar classifier was trained in order to compare its ability to recognize occlusions with respect to the Fast R-CNN. Fast R-CNN and DAG-CNN achieved 70.9% and 96.2% accuracy, respectively, Haar classifiers with about 50% accuracy, and an accuracy of grip and delivery of occluded objects of 80% and 90% in the virtual and physical application, respectively, was achieved.


DAG-CNN ; Fast R-CNN ; Haar classifiers ; Machine vision ; Manipulator robot; Occluded objects; VRML

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