文献类型： 会议论文

第一作者： Jie Zhang

作者： Jie Zhang ¹ ; Hao Xiong ² ; Hecang Zang ³ ; Meng Zhou ³ ; Dong Liu ¹ ; Zhonghua Liu ⁴ ; Hualei Shen ¹ ;

作者机构： 1.College of Computer and Information Engineering, Henan Normal University, China|Key Lab of Artificial Intelligence and Personalized Learning in Education of Henan, China|Big Data Engineering Lab of Teaching Resources & Education Quality Assessment of Henan, China

2.Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Australia

3.Institute of Agricultural Information Technology, Henan Academy of Agricultural Sciences, China

4.School of Information Engineering, Zhejiang Ocean University, China

关键词： Training;Accuracy;Fuses;Ear;Feature extraction;Yield estimation;Data mining

会议名称： [ "International Conference on Multimedia and Exposition" , "IEEE International Conference on Multimedia and Expo"]

主办单位：

页码： 1-6

摘要： Accurate wheat ears counting is crucial to the wheat yield estimation. Existing counting methods explore various architectures using density maps for training, and a few works also incorporate an auxiliary network. However, the small wheat ear with background noises makes its detection hard, and the auxiliary network methods tend to ignore interactions with its main network. To mitigate these issues, we propose a novel framework AuxSegCount which includes a segmentation auxiliary network and a main network for wheat ears counting. Unlike density map, the segmentation mask provides more local contexts of wheat ears. We therefore utilise it and introduce the segmentation attention module (SAM) that aims to capture local features around wheat ears. To promote the interactions, we further present the BiAttention Fusion Module (BiFM) that exploits both global information and local contexts into the main network. The experimental results on two datasets show the superiority of our method.

分类号： tp37