首页|Data on Intelligent Systems Reported by Researchers at Chinese Academy of Sciences (Spcs: a Spatial Pyramid Convolutional Shuffle Module for Yolo To Detect Occluded Object)
Data on Intelligent Systems Reported by Researchers at Chinese Academy of Sciences (Spcs: a Spatial Pyramid Convolutional Shuffle Module for Yolo To Detect Occluded Object)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
Springer Nature
A new study on Machine Learning - Intelligent Systems is now available. According to news reporting out of Shenyang, People's Republic of China, by NewsRx editors, research stated, "In crowded scenes, one of the most important issues is that heavily overlapped objects are hardly distinguished from each other since most of their pixels are shared and the visible pixels of the occluded objects, which are used to represent their features, are limited. In this paper, a spatial pyramid convolutional shuffle (SPCS) module is proposed to extract refined information from the limited visible pixels of the occluded objects and generate distinguishable representations for the heavily overlapped objects." Our news journalists obtained a quote from the research from the Chinese Academy of Sciences, "We adopt four convolutional kernels with different sizes and dilation rates at each location in the pyramid features and adjacently recombine their fused outputs spatially using a pixel shuffle module. In this way, four distinguishable instance predictions corresponding different convolutional kernels can be produced for each location in the pyramid feature. In addition, multiple convolutional operations with different kernel sizes and dilation rates at the same location can generate refined information for the corresponding regions, which is helpful to extract features for the occluded objects from their limited visible pixels. Extensive experimental results demonstrate that SPCS module can effectively boost the performance in crowded human detection."
ShenyangPeople's Republic of ChinaAsiaIntelligent SystemsMachine LearningChinese Academy of Sciences
NETL
NSTL
Springer Nature
