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

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. 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. Furthermore, 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 90% in the application, was achieved

単一運動性微生物の刺激応答計測のためのマイクロロボティックプラットホーム

九州工業大学博士学位論文 学位記番号:生工博甲第355号 学位授与年月日:令和元年9月20日1 Introduction|2 Observation Platform|3 Stimulation Platform|4 Application to Actual Motile Microorganisms|5 Conclusion九州工業大学令和元年