A lightweight target detection network: ghost-YOLONet
Yan He, Yuxin He, Chaoan Cai, Ye Zhang · 2024
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 [email protected] of 81.4%. Moreover, the FPS is improved, meeting the requirements of real-time detection tasks.