Abstract: The disclosure belongs to the field of visible light communication, and specifically discloses a stripe image processing method and apparatus for camera optical communication. The method includes: obtaining a stripe image data set, the stripe image data set including a stripe image sample and a stripe sequence label corresponding to the stripe image sample and a stripe-free image label; based on the stripe image sample and the stripe sequence label corresponding to the stripe image sample and the stripe-free image label, training a generative adversarial network to obtain an image reconstruction model and a stripe extraction model; among them, the image reconstruction model serves as a generator of the generative adversarial network, and the stripe extraction model serves as a discriminator of the generative adversarial network.

Abstract: The disclosure provides an LED light identification method, device, electronic apparatus, and storage medium, belonging to the technical field of indoor positioning and navigation. The method includes: obtaining a background frame and a spline frame by a CMOS camera in an LED lighting environment; determining a target quantity of dark stripes in the spline frame by comparing the background frame with the spline frame; determining a target switching frequency corresponding to the target quantity based on a predetermined corresponding relationship and the target quantity. The target switching frequency is used to mark LED lights. By determining the target switching frequency corresponding to the target quantity and determining the target LED lights having the target switching frequency, it can be determined that the current position of the CMOS camera is near the target LED light.

Abstract: The disclosure provides an LED light source recognition method, device, apparatus, and medium based on deep learning, belonging to the field of indoor positioning and navigation technology. The method includes the following. In an LED lighting environment, a spline frame is obtained through a CMOS camera, in which the spline frame is an image having dark stripes. The spline frame is input to a target detection model, a dark stripe detection result output by the target detection model is obtained. The multiple rectangular boxes are preprocessed, and based on the predicted classification corresponding to each preprocessed rectangular box, an image feature encoding sequence of the spline frame is determined. The image feature encoding sequence is compared with a light source feature encoding sequence corresponding to each LED light source to determine the light source feature encoding sequence that best matches the spline frame.