# A Super-Resolution Reconstruction Driven Helmet Detection Workflow
## 5 Discussion and Conclusions  
We propose an end-to-end helmet monitoring system, which implements a super-resolution reconstruction driven helmet detection workflow. It is designed for the scenario where the input image quality is limited, which is easily faced in engineering practice. For example, input images are acquired from a moving camera and transmitted through a bandwidth-limited wireless channel. Because of the limited bandwidth, images are always compressed and so have poor resolution and quality. The degrading of the input images will consequently decrease the helmet detection precision. To overcome this problem, the proposed SR driven helmet detection workflow consists of two sequential steps in the entire workflow. First, we use a super-resolution reconstruction module to improve the image resolution and quality instead of direct interpolation. Then, the processed images are fed into the detection module consisting of YOLOv5 to perform helmet detection. The two modules are trained separately from scratch and finetuned together, alternately. This is a typical multi-task learning strategy to help increase task specific accuracy by utilizing other tasks as constraints. Validation shows the effectiveness of our workflow. The comparison of the performance of different SR reconstruction methods shows that the proposed SR module could increase the PSNR value while maintaining a consistent SSIM value. The comparison of the performance of different detection workflows shows that the proposed SR module is effective at guiding the YOLOv5 and detection precision and AP are both increased. Generally speaking, based on current results, this will be a promising tool for helmet detection, which can be easily used in construction monitoring or traffic safety monitoring. Moreover, SR driven detection is a general workflow that is easy to be extended to other similar object detection tasks to solve the problem of performance degrading caused by poor inference input quality when the training input quality is good. Currently, our main idea is to use the individual model on specific tasks and combine tasks together. The model will be redundant if there are a large number of tasks. In the future, we will keep working on identifying a semantic subspace to attempt to remove the influence of image quality on detection performance.  
Conceptualization, Y.L. ([PERSON]) and Y.L. ([PERSON]); methodology, Y.L. ([PERSON]); software, Z.L.; validation, Y.L. ([PERSON]), Z.L. and Y.L. ([PERSON]); data curation, B.Z.; writing--original draft preparation, Z.L.; writing--review and editing, Y.L. ([PERSON]); visualization, B.Z.; supervision, J.H.; project administration, B.Z. and J.H.; funding acquisition, J.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Research on automatic and intelligent safety management technology at construction site, grant number CSCEC-2020Z-10 and Research on intelligent construction site management based on Internet of things and image recognition technology, grant number KJYF-2019-12.  
Figure 5: The PSNR and SSIM varied from the weighting of contextual loss.