# A Sample Balance-Based Regression Module for Object Detection in Construction Sites
## 6 Conclusions  
In this paper, we analyzed the bounding box redundancy problem of object detection in construction sites and found that there is still some room for improvement in the loss function of the regression module by introducing a balanced strategy of hard and easy samples. First, the existing loss functions all have certain defects that hinder the direction of the BBR. Second, the existing studies ignore the importance of hard and easy sample balance. As a result, the gradient of hard sample regression is too large, which limits the performance of BBR. Based on the above two problems, we propose the EfocalIL1-SEIOU loss to address the shortcomings of the existing loss function and balance the gradient of the hard and easy sample regression. Extensive experiments on the MOCS dataset show that EfocalIL1-SEIOU loss brings some improvements on many advanced object detectors, solves practical problems, and can be applied to various applications. In the future, we hope to design a low-light enhancement module and its loss function to perform low-light target detection tasks for building operation behaviors in scenes such as nighttime and tunnels.  
Conceptualization, X.W. and H.W.; methodology, X.W. and C.Z.; software, H.W.; validation, X.W., H.W. and C.Z.; investigation, X.W.; resources, Q.H.; data curation, Q.H. and L.H.; writing--original draft preparation, X.W.; writing--review and editing, X.W. and H.W.; visualization, X.W., C.Z. and L.H.; supervision, C.Z. and Q.H.; project administration, X.W. and H.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (no. 62072024 and 41971396), the Projects of Beijing Advanced Innovation Center for Future Urban Design (no. UDC2019033324 and UDC2017033322), R&D Program of Beijing Municipal Education Commission (KM202210016002), and the Fundamental Research Funds for Municipal Universities of Beijing University of Civil Engineering and Architecture (no. X20084 and ZF17061).  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Backbone** & **AP** & **AP\({}_{50}\)** & **AP\({}_{75}\)** & **AP\({}_{\mathbf{S}}\)** & **AP\({}_{\mathbf{M}}\)** & **AP\({}_{\mathbf{L}}\)** \\ \hline Faster R-CNN & ResNet-50-FPN & 0.458 & 0.730 & 0.501 & 0.171 & 0.364 & 0.564 \\ Faster R-CNN * & ResNet-50-FPN & 0.482 & 0.728 & 0.524 & 0.189 & 0.374 & 0.594 \\ Mask R-CNN & ResNet-50-FPN & 0.477 & 0.727 & 0.527 & 0.176 & 0.374 & 0.593 \\ Mask R-CNN * & ResNet-50-FPN & 0.486 & 0.727 & 0.526 & 0.172 & 0.385 & 0.602 \\ Cascade R-CNN & ResNet-50-FPN & 0.514 & 0.736 & 0.561 & 0.176 & 0.396 & 0.632 \\ Cascade R-CNN * & ResNet-50-FPN & 0.520 & 0.733 & 0.566 & 0.184 & 0.400 & 0.636 \\ YOLOF & ResNet-50 & 0.376 & 0.620 & 0.397 & 0.073 & 0.232 & 0.524 \\ YOLOF * & ResNet-50 & 0.389 & 0.622 & 0.409 & 0.061 & 0.254 & 0.541 \\ \hline \hline \end{tabular}
* indicates using the EfocalIL1-SEIOU loss instead of their original losses.  
\end{table}
Table 3: The performance when incorporating the EfocalL1-SEIOU loss with different SOTA models.  
Data Availability Statement: The dataset generated and analyzed during the current study can be obtained at [[http://www.anlab340.com/Archives/IndexArctype/index/t_id/17.html](http://www.anlab340.com/Archives/IndexArctype/index/t_id/17.html)]([http://www.anlab340.com/Archives/IndexArctype/index/t_id/17.html](http://www.anlab340.com/Archives/IndexArctype/index/t_id/17.html)) (accessed on 30 November 2020).  
The authors are thankful to all the personnel who either provided technical support or helped with data collection. We also acknowledge all the reviewers for their useful comments and suggestions.  
The authors declare no conflict of interest.