# Automatic Detection of Construction Workers' Helmet Wear Based on Lightweight Deep Learning
## 4 Discussion
### Performance Comparisons  
The comparison of quantitative experimental results using the same dataset and model training methods is reported in Table 4. Here, the helmet wear detection algorithm proposed in this paper is compared with SSD, Faster-RCNN, Yolov4 [32], YoloX series [33], and EfficientDet [34] series. Our proposed method can achieve 93.5% of mAP, which is an improvement compared to other detection methods. As shown in Figure 12, the mAP of our method is improved by 3.6% compared to EfficientDet-D3, which has a similar number of parameters. Although there is no lack of YoloX-X and EfficientDet-D5 with higher mAP among these algorithms, our method is not only more accurate compared to them, but the model size is only 11.3% and 33.3% of theirs. Our method can stabilize around 42 fps for the time factor, while YoloX-X and EfficientDet-D5 are only 13 and 20 fps, respectively. In terms of processing speed, our method has an excellent improvement. This is because many network topologies have too many unnecessary parameters, leading to slow convergence during model training, and may overfit and affect the accuracy of detection results. Our proposed network structure can achieve higher accuracy with smaller model size. In Figure 13, we show some detection examples using the model proposed in this paper at several real construction sites in Korea. The results are shown using non-maximum suppression and setting the threshold score above 0.5. These examples cover many factors, including visual range, illumination, individual pose, and occlusion. These visualization \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Input Size**} & \multirow{2}{*}{**Backbone**} & \multirow{2}{*}{**Parameters** **(Millions)**} & \multirow{2}{*}{**FPS**} & \multicolumn{2}{c}{**AP (\%)**} & \multirow{2}{*}{**mAP** **(\%)**} \\ \cline{5-6}  & & & & **Helmet** & & **Person** & **(\%)** \\ \hline SSD & 300 \(\times\) 300 & VGG16 & 26.2 & 18 & 85.9 & 78.3 & 82.1 \\ \hline Faster-RCNN(VGG) & 600 \(\times\) 600 & VGG16 & 136.7 & 4 & 80.1 & 74.7 & 77.4 \\ Faster-R-CNN(ResNet) & 600 \(\times\) 600 & ResNet50 & 28.3 & 17 & 85.5 & 81.5 & 83.5 \\ \hline YOLOX-Tiny & 640 \(\times\) 640 & Modified CSP & 5.1 & 42 & 86.4 & 81.8 & 84.1 \\ YOLOX-S & 640 \(\times\) 640 & Modified CSP & 8.7 & 32 & 88.7 & 83.7 & 86.2 \\ YOLOX-M & 640 \(\times\) 640 & Modified CSP & 25.2 & 19 & 90.2 & 85.6 & 87.9 \\ YOLOX-L & 640 \(\times\) 640 & Modified CSP & 54.1 & 15 & 93.1 & 88.1 & 90.6 \\ YOLOX-X & 640 \(\times\) 640 & Modified CSP & 99.1 & 13 & 94.9 & 89.7 & 92.3 \\ YOLOv4 & 416 \(\times\) 416 & CSPDark-53 & 64.3 & 18 & 93.2 & 86.2 & 89.7 \\ \hline EfficientDet-D0 & 512 \(\times\) 512 & Efficient-B0 & 3.8 & 35 & 78.8 & 72.6 & 75.7 \\ EfficientDet-D1 & 640 \(\times\) 640 & Efficient-B1 & 6.5 & 31 & 84.3 & 81.5 & 82.9 \\ EfficientDet-D2 & 768 \(\times\) 768 & Efficient-B2 & 8.1 & 30 & 91.5 & 85.5 & 88.5 \\ EfficientDet-D3 & 896 \(\times\) 896 & Efficient-B3 & 11.9 & 27 & 92.5 & 87.3 & 89.9 \\ EfficientDet-D4 & 1024 \(\times\) 1024 & Efficient-B4 & 20.6 & 24 & 92.9 & 88.1 & 90.5 \\ EfficientDet-D5 & 1280 \(\times\) 1280 & Efficient-B5 & 33.6 & 20 & 93.7 & 89.5 & 91.6 \\ \hline Our Approach & 416 \(\times\) 416 & GhostNet & 11.2 & 42 & **95.9** & **91.1** & **93.5** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of quantitative experimental results on the same test set, where bold numbers indicate the best value in each column. “Helmet” and “Person” columns indicate whether the individual wears a safety helmet or not, respectively. Figure 11: The visualized heat maps were generated by adding different attention mechanisms, as in Table 3. The visualization results are compared for each of the three different size feature maps obtained by MSFFN (**a**) 13 \(\times\) 13, (**b**) 26 \(\times\) 26, and (**c**) 52 \(\times\) 52.  
results demonstrate the excellent detection capability of our proposed model, which can be well generalized to various construction sites. Figure 12: Model size vs. mAP.