Graphic Visualization and Recognition System Based on Electroluminescent Devices and Robotic Arm
Alternating current electroluminescent (ACEL) devices with a pyramidal conical structure luminescent layer were fabricated and integrated with a convolutional neural network (CNN) to construct an image recognition system for robotic arm applications. The ACEL device serves as a flexible, low-power, homogeneous light source, enabling multispectral imaging that mitigates ambient light interference. Images captured by an ESP32-CAM module are processed by a deep learning model, achieving a recognition accuracy of 96.7% for seven distinct shapes (rectangle, triangle, circle, star, butterfly, flower, snowflake). The system demonstrates high brightness, high contrast, and flexibility, addressing limitations of traditional image recognition systems that rely on hand-designed features and are susceptible to illumination variations. This work validates the potential of ACEL-based multispectral imaging for robust environment perception in dynamic scenarios, offering a pathway toward more efficient and reliable robotic vision systems.