# DLMFCOS: Efficient Dual-Path Lightweight Module for Fully Convolutional Object Detection
## 5 Conclusions
The data presented in this study are openly available online: [[http://host.robots.ox.ac.uk/pascal/VOC/voc2007/index.html](http://host.robots.ox.ac.uk/pascal/VOC/voc2007/index.html)]([http://host.robots.ox.ac.uk/pascal/VOC/voc2007/index.html](http://host.robots.ox.ac.uk/pascal/VOC/voc2007/index.html)), [[http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html)]([http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html)) (accessed on 4 December 2022) [26], and [[https://cocodataset.org/#home](https://cocodataset.org/#home)]([https://cocodataset.org/#home](https://cocodataset.org/#home)) (accessed on 4 December 2022) [27]. The present Research has been conducted by the Research Grant of Kwangwoon University in 2022. The authors declare no conflict of interest. The research has been conducted by the Research Grant of Kwangwoon University in 2022. * (12) [PERSON]; [PERSON] YOLO9000: Better, Faster, Stronger. In Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 21-26 July 2017; pp. 6517-6525. [CrossRef]
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