# MCA-YOLOV5-Light: A Faster, Stronger and Lighter Algorithm for Helmet-Wearing Detection
## 4 Experimental Analysis and Analysis
### Experimental Results and Analysis  
#### 4.2.1 MCA-YOLOV5s Fusion Analysis  
In order to further fairly verify that the application of the MCA attention module to the backbone network location of the YOLOV5 model can extract more abundant features, this paper integrates the MCA attention module to different positions of the network model and studies the detection results. According to the YOLOv5s network model structure, the MCA attention module is separately fused in three regions of YOLOv5s backbone network, neck and prediction module. Since MCA modules are enhanced in important channels and spatial locations, this paper integrates the MCA attention module into each of the above three parts to produce three new network models based on the YOLOv5s algorithm: MCA-YOLOv5-BackBone, MCA-YOLOv5-Neck and MCA-YOLOv5-Prediction. Figure 7 shows the specific location of the MCA attention module fusion network. In Figure 7a, in the backbone network of YOLOv5s, the MCA attention module is fused to CSP1_3 (i.e., feature fusion); in Figure 7b, the MCA attention module is fused to the neck Concat layer of YOLOv5s and in Figure 7c, one MCA attention module is fused before the convolution of each prediction module of YOLOv5s. The results of the experimental comparison between the fused MCA attention modules in three different positions and the unfused MCA attention modules in three different locations are specifically shown in Table 2. Secondly, the outputs of the same channels of the three fusion design networks are compared. Subsequently, in order to observe the detection capability of the network integrating the MCA attention module relatively clearly on the targets of various sizes, in this paper the instances are classified into the following three proportional types according to the target size: small target (target area 32\({}^{2}\)), medium target (32\({}^{2}\) < Target area of 96\({}^{2}\)) and large target (target area > 96\({}^{2}\)). Among them, the IoU threshold of 0.5 is used for both the large and medium targets and the mAP evaluation index. Precision (P) is the correct ratio of the true values in the data predicted to be correct, and Recall (R) presents how much of the data with correct true values can be predicted correctly. Means Average Precision (mAP) refers to calculating the area under the PR curve enclosed by Precision and Recall and then averaging the categories to evaluate the detection accuracy of multiple categories. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Itemize** & **Edition** \\ \hline \multirow{2}{*}{Hardware configuration} & system & Ubuntu 18.04 \\  & Graphics card & GeForce RTX 2080 Ti \\ \hline \multirow{3}{*}{Software configuration} & CPU & AMD Ryzen 7 3800X 8-Core \\  & Python Version & 3.8 \\ \cline{1-1}  & Deep learning framework & Pytorch \\ \cline{1-1}  & CUDA & 10.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experiment Operating Environment (List of equipment and versions required for the experiment). We analyzed the different effects of different positions of the MCA embedded model in the experimental results. Compared with the deep network, the backbone network has less semantic information but still contains the texture information and contour information that are easily ignored in the middle and low layers of the target. Moreover, the shallow information of the backbone network contains rich location information. The fusion of the MCA module in the backbone network can better fuse the easily ignored spatial features, rich location information and channel features of small objects in the feature map, thereby enhancing the feature information. By contrast, in the deeper neck of the network and