Sistem Deteksi Halangan Arsitektural pada Kendali Kursi Roda Pintar menggunakan HOG dan ANN berbasis TX2 - Muhammad Ibrahim Kumail; Fitri Utaminingrum (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2022)
People with disabilities in Malang City reached 687 of the population based on data from the Badan Pusat Statistik (BPS) Malang in 2020 data. The disabled person with quadriplegia is a condition with limitations to walking normally, so a wheelchair is needed. The development of electric wheelchairs is one of the focuses of developing this research to be able to walk automatically because, under certain conditions, disabled people have limited arms that cannot control a wheelchair independently. However, an intelligent wheelchair safety feature is needed against the existing architectural obstacles in public facilities. This study applies digital image processing using the Histogram of Oriented Gradient (HOG) method as a feature extraction method to obtain special features on objects and the Artificial Neural Network (ANN) method as an object class classification, with the implementation of image recording around the research site, the results of obstacle detection are obtained. Architectural design with an average accuracy of 79.72% in conditions of recording distance of 4m, 3m, and 2m detection of pillar objects, while at the duplicate recording distance detection of stairs objects get 73.89% and if the condition is detected as an obstacle object, then the condition of the chair the wheels change to a stop slowly with the PWM value decreasing in decrement to 0 to ensure the intelligent wheelchair avoids obstruction.