Optimized Rearealization of YOLOv3 Algorithm Based on Embedded Terminal
Image object recognition technology is a hot issue in the field of computer vision research.However,most of the cur-rent advanced object detection algorithms are based on server-side training and deployment.Under the background of today's mobile Internet era,they cannot be truly applied.At the same time,taking into account the needs of localized chips and software develop-ment environment,the YOLOv3 detection model is optimized and trained,and the model is deployed based on the embedded termi-nal,the Baidu EdgeBoard Edge AI Computing Box.Results of experiment fully show that the optimized YOLOv3-MobileNetv1 mod-el has a good detection and recognition effect on pedestrians,vehicles,airplanes and other types of objects.
embedded terminalobject detectiondeep learninglightweight model
国家科技期刊平台
NSTL
万方数据
