# Target State Classification by Attention-Based Branch Expansion Network
## 6 Conclusions and Future Work  
In this work, we collect two new datasets from our digital assembly test center (DTAC) for the monitoring of laboratory technical states. These two datasets have a small amount of data; the change of illumination, background, type, etc., result in a large intra-class difference and smaller inter-class difference. We propose a generic, attention-based branch expansion network (ABE) by reorganizing and modifying the core structure of Xception and embedding the dual-attention module with a parallel residual connection. The ABE networks consist of linearly stacked blocks with parallel branches, each of which is followed by a plug-and-play attention module with the negligible overhead of parameter and computation. We conduct detailed ablation studies and carry out full horizontal contrast and vertical contrast. The \"ABE 2-4\" model with two stacked blocks, each with four branches, performs best among the four attention-added structures with different depths and widths. We reach an accuracy of 94.53%, which is comparable to the SOTA technologies on Trashnet.  
Our method has a wide application prospect. In addition to the classification of technical states in the laboratory, it can also be applied to dress pattern classification, trash classification, medical diagnosis, etc. However, there are still some limitations. First of all, the category and quantity of datasets are small. In addition, this is a single-image classification model, which is not enough to be applied in real multi-target scenes. To further improve the model performance, a trained YOLO model can be used as a front end in practical application, and the generated bounding boxes can serve as the input of our model. Our dataset acquisition method, Fly_arounds video, fits the end-to-end requirement.  
Conceptualization, S.S.; methodology, Y.Z. and S.S.; software, Y.Z., H.L. and L.L.; validation, Y.Z. and G.L.; formal analysis, Y.Z. and S.S.; investigation, Y.Z. and G.L.; resources, S.S.; data curation, Y.Z. and D.L.; writing original draft preparation, Y.Z.; writing review and editing, Y.Z. and S.S.; visualization, Y.Z.; supervision, S.S.; project administration, S.S. and Y.Z.; funding acquisition, S.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Ethical review and approval were waived for this study, because this paper does not involve human or animal research. It studies scene understanding and image classification in the computer vision field; the wearable state classification is one of the image classification tasks, not medical pathology research.  
Not applicable.  
Figure 9: Confusion matrix of the original Xception and the final “ABE 2-4” with 2 linear blocks followed by a dual-attention module each, with 4 parallel branches in each block.  
Data Availability Statement: Publicly available datasets were analyzed in this study. These data can be found here: [[[https://github.com/garythung/trashnet](https://github.com/garythung/trashnet)]([https://github.com/garythung/trashnet](https://github.com/garythung/trashnet)) (accessed on 6 June 2021)].  
The authors would like to acknowledge [PERSON] and [PERSON] for making their datasets available.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ABE & Attention-Based Branch Expansion Network \\ SVM & Support Vector Machine \\ RF & Random Forest \\ KNN & K-Nearest Neighbor \\ BAM & Bottleneck Attention Module \\ CBAM & Convolutional Block Attention Module \\ CNN & Convolutional Neural Network \\ GAP & Global Average Pooling \\ GMP & Global MAX Pooling \\ Grad-CAM & Gradient-weighted Class Activation Mapping \\ \end{tabular}