Design and implementation of a control system based on gesture recognition using the YOLO algorithm

Gesture recognition, as a non-contact and intuitive human-computer interaction, has a unique advantage in the field of home control. This paper proposes a gesture recognition for home control based on YOLOV8, and the YOLOV8 model is one of the modern advanced object detection models. YOLOV8 model has great potential in the field of gesture recognition due to its advantages of high precision, real-time performance and adaptability. Specific gestures are collected using OPENMV for data acquisition, and pre-processed by color smoothing, canny edge detection algorithm, adaptive threshold algorithm, morphology closing operation, sharpening, etc. The processed images were imported into the YOLOv8 model for training, the trained YOLOv8 model was used for gesture recognition, the recognition results were converted into string format, and the recognition results were sent to the STM32F103 development board through serial communication to further control the home appliance.