Capsule Neural Network based Height Classification using Low-Cost Automotive Ultrasonic Sensors

High performance ultrasonic sensors are commonly used in medical applications. Ultrasonic sensors are also used in autonomous driving for near-field sensing. Its low cost is one of its main advantages and thus it is necessary to have low computational complexity for the classification algorithms. In this paper, we propose an efficient capsule neural network to provide a detailed height analysis of detected objects. We apply a novel re-sorting and re-shaping method to feed the neural network with ultrasonic data. We evaluated the proposed method under different environmental conditions and obtained high validation accuracy of 99% at a run-time of 0.2 ms. We compare it with a conventional CNN and demonstrate better accuracy and run-time.