Target State Classification by Attention-Based Branch Expansion Network

Abstract

The intelligent laboratory is an important carrier for the development of the manufacturing industry. In order to meet the technical state requirements of the laboratory and control the particle redundancy, the wearing state of personnel and the technical state of objects are very important observation indicators in the digital laboratory. We collect human and object state datasets, which present the state classification challenge of the staff and experimental tools. Humans and objects are especially important for scene understanding, especially those existing in scenarios that have an impact on the current task. Based on the characteristics of the above datasets—small inter-class distance and large intra-class distance—an attention-based branch expansion network (ABE) is proposed to distinguish confounding features. In order to achieve the best recognition effect by considering the network’s depth and width, we firstly carry out a multi-dimensional reorganization of the existing network structure to explore the influence of depth and width on feature expression by comparing four networks with different depths and widths. We apply channel and spatial attention to refine the features extracted by the four networks, which learn “what” and “where”, respectively, to focus. We find the best results lie in the parallel residual connection of the dual attention applied in stacked block mode. We conduct extensive ablation analysis, gain consistent improvements in classification performance on various datasets, demonstrate the effectiveness of the dual-attention-based branch expansion network, and show a wide range of applicability. It achieves comparable performance with the state of the art (SOTA) on the common dataset Trashnet, with an accuracy of 94.53%.