Ghost-YOLO： Lightweight Masked Face Detection Algorithm

‍ ‍In embedded devices， due to the limitation of low computing power and small storage space， the inference speed of current large-scale high-precision object detection algorithms is low. For that， this paper designed a lightweight masked face detection algorithm. Firstly， a high active ghost （HAG） module was designed to reduce the redundancy in feature learning with a light computational cost. Secondly， based on HAG， a high active ghost cross stage partial （HAG-CSP） connection module was designed to improve the feature learning ability. Thirdly， the HAG-CSP was used to lightweight the you only look once （YOLO） model to raise a Ghost-YOLO model for masked face detection. Experimental results show that the speed of proposed method is 24.72 milliseconds per image for 640×640 sized images on a NVIDIA Jetson NX embedded device and reduces the number of parameters， under the condition that the proposed method had a better accuracy performance than existing object detection models.