A lightweight target detection network: ghost-YOLONet

He Yan; Yuxin He; Chaoan Cai; Ye Zhang

doi:10.1117/12.3029829

21 June 2024 A lightweight target detection network: ghost-YOLONet

He Yan, Yuxin He, Chaoan Cai, Ye Zhang

Proceedings Volume 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024); 131673C (2024) https://doi.org/10.1117/12.3029829
Event: International Conference on Remote Sensing, Mapping and Image Processing (RSMIP 2024), 2024, Xiamen, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

He Yan, Yuxin He, Chaoan Cai, and Ye Zhang "A lightweight target detection network: ghost-YOLONet", Proc. SPIE 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024), 131673C (21 June 2024); https://doi.org/10.1117/12.3029829

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KEYWORDS

Feature extraction

Detection and tracking algorithms

Target detection

Image processing

Performance modeling

Object detection

Feature fusion

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