Sensor Data Fusion Integration in Quantum Discrete Transform for Object Classification

Abstract

In recent years, with rapid progress in the development of quantum technologies, quantum machine learning has attracted a lot of
interest. In particular, a family of hybrid quantum-classical neural networks, consisting of classical and quantum elements, has been massively
explored. The proposed sensor data fusion integration in quantum discrete transform for object detection from sensor data classification is
described in this paper. The proposed quantum object detection for sensor data classification (QODSDC) algorithm and model are presented.
The proposed QODSDC can be used to detect the objects and classify it into relevant classes. The features extracted from the objects for
classification. The evaluation of the proposed approach is carried out for detection and classification of car, truck, person, bicycle, and traffic
lights. The performance of the proposed QODSDC is carried out on the basis of the precision, recall and F1-measure. More than 80% precision
is obtained in the detection and classification of objects like car, truck and bicycle. The velocity of the objects are taken into consideration
during object detection and classification.