Animal Pose Estimation Algorithm Based on the Lightweight Stacked Hourglass Network

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.