A LED Module Number Detection for LED Screen Calibration

Abstract

In this paper, an LED (Light-Emitting Diode) screen number detection method is proposed to guide the autonomous navigation of UAV (Unmanned Aerial Vehicle) for further task implementation. Aiming at the LED module identification in the task of detecting bright and dark lines on the LED screens, an improved YOLOv5 object detection algorithm has been developed with a series of operations, i.e., the backbone network, activation function, optimization strategy and post-processing screening prediction box. The LED dataset is designed, along with data augmentation so as to achieve a high detection accuracy of the trained detection model in too strong or too dark light complex scenes. The trained model can be transplanted to the Android platform, which provides support for subsequent UAV automatic navigation and portable screen calibration. With extremely high detection accuracy (0.843 mAP@0.5), the detection speed of the proposed model is also quite fast, achieving almost real-time performance (85 FPS).