In response to the demand for human detection in the lighting system,an embedded machine vision system based on improved YOLOv3is proposed to realiz e human detection and positioning.An infrared human body image is collected by an infrared camera to make a data set;YOLOv3is improved and trained to obtain a lightweight human body detection model,and the spatial coordinates of the hum an body are obtained from the rectangular frame coordinates of the human body th rough the grid map method.The accuracy of the improved YOLOv3human body detect ion model has increased by 3.26%,the amount of parameters has been reduced by 96.01%,the amount of calculation has been reduced by 91.04%,and the volume of t he model has been reduced by 96.00%.After model conversion,it is deployed on t he K210embedded platform with a neural network processor to achieve accurate hu man detection and positioning.The system was tested in the actual lighting envi ronment,and the detection speed of the system reached 16frames per second and achieved good detection results.The experimental results show that the human de tection and positioning method proposed in this paper can be implemented on an e mbedded platform and has good performance.