Phenphitcha Patthanajitsilp and Prabhas Chongstitvatana
Int Conf. on Knowledge and Smart Technology (KST 2021).
Jan 26-29, 2022, Bangkok, Thailand
This research aims to present the detection system of an obstacle for electric wheelchair using computer vision in order to facilitate for disabled persons and reduce the possibilities of accidents. In this system, the distance threshold is set to alert when a wheelchair is approaching an obstacle. The alert system consists of the smartphone's camera attached to the back of a wheelchair. The YOLOv3 model was used for object detection. The researcher has developed an algorithm to detect obstacles such as pillars, doors, or edge of the wall with edge detection method to enhance the detection efficiency of the system. Therefore, the usage of two algorithms enables the system to choose the obstacle detection between objects and edge detection. The research found that the system can choose the algorithm to detect obstacles with an accuracy of up to 80%. Moreover, the experiment revealed that the system can alert warnings before collisions with an accuracy of up to 90%. Further, this system can also calculate the approximate time prior to the collision.
index by IEEE explore https://doi.org/10.1109/KST53302.2022.9729083