Animal Pose Estimation Algorithm Based on the Lightweight Stacked Hourglass Network
Research Square (Research Square)(2022)
摘要
Abstract Pose estimation has been a hot topic in the field of machine vision in recent years. In the pose estimation task, a lightweight stacked hourglass network (SHN) algorithm is proposed. Moreover, aiming at the problem of large parameters in depthwise convolutional neural networks, a lightweight residual module is proposed, that is, based on the lightweight efficient channel attention improved conditional channel-weighted method (ICCW-Bottle), which replaces bottleneck module, thereby reducing the weight of the network and obtaining the feature information of different scales. Given the problem that a large amount of feature information is easily lost after the network pooling operation, a lightweight dual-branch fusion module is proposed that fully integrates high-level semantic information and low-level detailed features under the condition of a small number of parameters. Finally, the training model of synthetic animal dataset and real animal dataset was jointly applied. Compared with the consistency-constrained semi-supervised learning (CC-SSL) method, the proposed method increased in accuracy of pose estimation by 5.5%. It also reduced the number of network parameters and the calculation amount. The results of the ablation experiment verify the advancement and effectiveness of the overall network.
更多查看译文
关键词
animal pose estimation algorithm
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要