Aiming at the complexity and large parameter size of you only look once version four (YOLO v4) object detection network, which cannot meet the requirements of lightweight deployment and real-time computation on mobile devices, a lightweight object detection network called Ghost-YOLONet is proposed. Firstly, the backbone network of YOLO v4 is replaced by the lightweight network GhostNet, and the decoupled fully-connected attention mechanism is integrated into the Ghost module to better capture global information. Then, the parallel structure of the spatial pyramid pooling module in YOLO v4 is changed to a serial structure to improve the model's execution efficiency. Comparative experimental results on the PASCAL VOC2007 and VOC2012 datasets show that compared with the YOLO v4 model, Ghost-YOLONet reduces the parameter size by 81.1%, the model volume by 81.5%, and achieves mAP@0.5 of 81.4%. Moreover, the FPS is improved, meeting the requirements of real-time detection tasks.
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