# Single-Shot Global and Local Context Refinement Neural Network for Head Detection
## 5 Conclusions and Future Work  
Although head detection is a fundamental and important task, the current approaches fall short in terms of their ability to extract useful local and global contexts. We suggest CRNet, which is a one-stage detector for head detection, as a remedy for this issue. CRNet identifies heads via their center points in the images, without any anchor design, avoiding the need for prior statistical analysis. Because we associate heads with their center points, the similarity between points replaces the similarity between head proposals, and the computational costs become irrelevant to the number of targets. In order to extract the global context better and improve the classification ability, we introduce the global context refinement module (GRM). The GRM establishes long-range dependencies between pixels through SNL. SNL is much easier to optimize than the previous approach to obtaining global contexts through the use of repeated convolution layers [12]. In addition, we enhance the global context by fusing output feature maps of different GRMs. Furthermore, a multi-scale dilated convolution layer is also used to refine the local context around the center point of the head. The computational complexity of the dilated convolution with a dilation rate greater than 1 is less than that of a conventional convolution with the same receptive field. The results of our experiments demonstrate that our proposed method achieves state-of-the-art performance on two public datasets. The running time test proves that our method achieves a good balance in terms of speed and accuracy. In future work, we intend to expand our framework to other visual fields, such as human crowd counting and face detection. At the same time, inspired by the latest progress in deep learning technology, in future work, we will explore the differences in our models' performance caused by different backbones, especially ViT backbones [44].  
Conceptualization, Z.Y.; methodology, J.H.; software, J.H.; validation, J.H. and Z.Y.; formal analysis, J.H.; investigation, J.H.; resources, Z.Y.; data curation, J.H.; writing--original draft preparation, J.H.; writing--review and editing, J.H. and Z.Y.; visualization, J.H.; supervision, Z.Y.; project administration, Z.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The Brainwash Dataset can be found at [[https://exhibits.stanford.edu/data](https://exhibits.stanford.edu/data)]([https://exhibits.stanford.edu/data](https://exhibits.stanford.edu/data)) (accessed on 22 November 2022 ), and the HollywoodHeads Dataset can be downloaded from [[https://www.robots.ox.ac.uk/~vgg/software/headmview/](https://www.robots.ox.ac.uk/~vgg/software/headmview/)]([https://www.robots.ox.ac.uk/~vgg/software/headmview/](https://www.robots.ox.ac.uk/~vgg/software/headmview/)) (accessed on 22 November 2022).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{p{85.4 pt} p{85.4 pt} p{85.4 pt} p{85.4 pt}} \hline \hline
**Model** & **Backbone** & **AP (\%)** & **FPS** \\ \hline SSD & VGG16 & 74.1 & 56.0 \\ \hline Faster RCNN & ResNet50 & 91.9 & 15.4 \\ \hline SANM & ResNet50 & 93.12 & 15.2 \\ \hline CRNet w/o GRM & ResNet50 & 92.22 & 48.5 \\ \hline CRNet w/o LRM & ResNet50 & 93.47 & 49.3 \\ \hline CRNet (proposed) & ResNet50 & 93.89 & 47.7 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The results of running time tests.