An object detection system using array planar capacitive sensors

Abstract

This paper presents a target detection system using array-based planar capacitive sensors, demonstrating excellent detection performance in gesture recognition. The system achieves gesture target classification by collecting the capacitance values of the planar capacitive sensor array and optimizing the BP neural network through a genetic algorithm for model training. Additionally, the system employs visual detection for gesture targets, using transfer learning with the MobileNet V2 network in the Keras framework to achieve gesture target classification. The system utilizes a data fusion mechanism to merge visual recognition and planar capacitive sensor array recognition for decision-making. Experimental results demonstrate that the target detection system exhibits outstanding detection performance in gesture recognition, with an average accuracy of up to 98.92%.