# Multi-Scale Safety Helmet Detection Based on SAS-YOLOv3-Tiny
## 4 Experiments and Results Analysis
### Experimental Progress and Result Analysis
#### 4.2.2 Ablation Experiments  
In this section, to better understand the influence of each improved method on the detection effect, ablation learning is carried out on the self-built helmet validation set. First of all, we first presented each of our schemes in Table 2. Then, we compared different modification schemes based on YOLOv3-tiny in terms of indicators including P, R, F1, mAP, Weight, Total Parameters and average time of detecting a single image (detection time) in Table 3, and comprehensively analyze how each improvement point promote performance. Finally, we demonstrated the effectiveness of the improvement point by presenting a training curve for each scheme. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Scheme** & **SR** & **3-Scale** & **SPP** & **SE** & **CIoU** \\ \hline SR & \(\surd\) & & & & \\ SR-3s & \(\surd\) & \(\surd\) & & & \\ SR-3s-SPP & \(\surd\) & \(\surd\) & \(\surd\) & & \\ SR-3s-SPP-SE & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \\ SR-3s-SPP-SE-CIoU (Ours) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Different improvement schemes. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **P/\%** & **R/\%** & **mAP/\%** & **F1/\%** & **Weight/MB** & **Total Parameters/10\({}^{6}\)** & **Detection Time/ms** \\ \hline YOLOv3-tiny & 70.7 & 73.3 & 73.7 & 71.9 & 69.5 & 8.67681 & 2.5 \\ SR & 69.3 & 77.9 & 78.2 & 73.3 & 36.5 & 4.53490 & 2.8 \\ SR-3s & 69.6 & 79.6 & 80.2 & 74.2 & 39.2 & 4.86658 & 3.0 \\ SR-3s-SPP & 70.3 & 80.4 & 80.1 & 75.0 & 45.4 & 5.65301 & 3.1 \\ SR-3s-SPP-SE & 72.3 & 80.2 & 81.2 & 76.0 & 46.9 & 5.82773 & 3.2 \\ SR-3s-SPP-SE-CIoU (Ours) & 73.2 & 80.2 & 81.6 & 76.4 & 46.9 & 5.82773 & 3.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ablation results of different models on the validation set. The different schemes are shown in Table 2. We used the yolov3-tiny algorithm as the baseline. Specifically, in the scheme SR, the Sandglass-Residual (SR) module was used to replace the original convolution layer, and the max-pooling layer was replaced with the convolution layer of stride two. In the scheme SR-3 scale (3 s), on the basis of the scheme SR, a three-scale prediction method was adopted. In the Scheme SR-3s-SPP, to further improve the detection effect, the improved SPP was utilized on the basis of the Scheme SR-3s. In addition, in the Scheme SR-3s-SPP-SE, the Squeeze-Excitation (SE) channel attention mechanism was integrated into the Sandglass-Residual module to extract more representative features on the basis of the Scheme SR-3s-SPP. In the Scheme SR-3s-SPP-SE-CIoU, we used CIoU loss on the basis of the Scheme SR-3s-SPP-SE. A combination of five improvements formed our final algorithm, in other words, the last Scheme SR-3s-SPP-SE-CIoU was our improved algorithm (named SAS-YOLO-v3-tiny). The ablation results of different models on the validation set are shown in Table 3. From Table 3, we can see that the values of indicators including P, R, mAP, F1 are low in the original YOLOv3-tiny algorithm. Additionally, the indicators, including the weight size of the model and total parameters, still have room for improvement. Compared with the original algorithm, the improved YOLOv3-tiny based on the Sandglass-Residual module made the network more lightweight; this was because the Scheme SR based on depthwise separable convolution reduced the number of parameters and computation amount, reducing the size of weight files and the number of parameters by nearly half. In addition, owing to putting the shortcut on the high-dimensional representations, the SR module could extract rich feature, which could increase R by 4.6%, increase mAP by 4.5% and increase F1 by 1.4% while keeping the detection speed almost unchanged. The Scheme SR-3s changed two-scale feature prediction into three-scale feature prediction, which could incorporate shallow features with sufficient location information, making R, mAP, F1 increase by 1.7%, 2%, 0.9%, respectively.