## 3 Results
### Contrast Experimental Results  
In order to better analyze our proposed improved algorithm, we conducted comparative experiments using some lightweight object detection algorithms. The quantitative analysis was also carried out in terms of both the accuracy of recognition and the performance of the algorithms. The results of the comparison experiments are shown in Tables 6 and 7. Based on the results of the contrast experiments, the following conclusions can be reached. 1. In Table 6, the proposed model's precision, recall, mAP (0.5), mAP (0.75) values, and F1-scores were 88.89%, 87.12%, 50.91%, and 88.00%, respectively. 2. From Table 6, we can see that our proposed yolov4_RMLight_ADD model has high precision and mAP (0.5) values, with precision values higher than the lightweight network YOLOv4_tiny, Eifficientdet-d0 [49], YOLOv5n, and YOLOv5s by 30.97%, 10.06%, 13.17%, and 13.01%. The mAP (0.5) values are 29.34%, 28.99%, 20.36%, and 18.64% higher than those of the lightweight networks YOLOv4_tiny, Eifficientdet-d0, YOLOv5n, and YOLOv5s, respectively, and the precision value is 0.26% higher than the large network YOLOv4. The mAP (0.5) value is only 2.23% lower than YOLOv4. This shows that our proposed yolov4_RMLight_ADD model has excellent object detection accuracy. 3. From Table 6, we can see that our proposed yolov4_RMLight_ADD model has higher recall and F1 values, with recall values 30.84%, 33.85%, and 23.75% higher than the lightweight networks YOLOv4_tiny, Eifficientdet-d0, YOLOv5n, and YOLOv5s \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithms** & **Precision** & **Recall** & **mAP (0.5)** & **mAP (0.75)** & **F1** \\ \hline
**yolov4_RMLight_ADD** & **88.89\%** & **87.12\%** & **86.84\%** & **50.91\%** & **88.00\%** \\ YOLOv4 & 88.63\% & 89.48\% & 89.07\% & 49.20\% & 89.40\% \\ YOLOv4_tiny & 57.92\% & 56.28\% & 57.50\% & 44.92\% & 57.00\% \\ Eifficientdet-d0 & 78.83\% & 53.27\% & 57.85\% & 51.99\% & 56.20\% \\ YOLOv5n & 75.72\% & 63.37\% & 66.48\% & 52.07\% & 67.20\% \\ YOLOv5s & 75.88\% & 65.47\% & 68.20\% & 52.35\% & 69.00\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Accuracy results for different detection models. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithms** & **Model Size/MB** & **Parameter Quantity/Million** & **Flops** \\ \hline
**yolov4_RMLight_ADD** & **42.50** & **10.80** & **3.53 G** \\ YOLOv4 & 244 & 64.36 & 30.17 G \\ YOLOv4_tiny & 22.25 & 6.06 & 3.47 G \\ Eifficientdet-d0 & 15 & 3.87 & 2.55 G \\ YOLOv5n & 6.95 & 1.88 & 2.33 G \\ YOLOv5s & 27.1 & 7.28 & 8.53 G \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance results for different detection models. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithms** & **mAP (0.5)** & **Model Size/MB** & **Flops** \\ \hline
**yolov4_RMLight_ADD** & **86.54\%** & **42.50** & **3.53 GB** \\ yolov4_mobilenetv1 & 79.46\% & 51.10 & 5.27 GB \\ yolov4_mobilenetv2 & 79.68\% & 46.60 & 4.08 GB \\ yolov4_mobilenetv3 & 79.68\% & 46.60 & 3.80 GB \\ yolov4_resnet50 & 65.36\% & 127 & 33.68 GB \\ yolov4_vgg16 & 66.93\% & 90 & 23.94 GB \\ \hline \hline \end{tabular}
\begin{tabular}{c} Yolov4_mobilenetv1 indicates that the backbone feature extraction network is replaced with mobilenetv1. Yolov4_mobilenetv2 indicates that the backbone feature extraction network is replaced with mobilenetv2. Yolov4_mobilenetv3 indicates that the backbone feature extraction network is replaced with mobilenetv3. Yolov4_resnet50 indicates that the backbone feature extraction network is replaced with Resnet50. Yolov4_vgg16 denotes replacing the backbone feature extraction network with VGG16.  
\end{table} 
Table 5: The experimental results for improving the effectiveness of the backbone feature extraction network. (21.65%). The F1 values are 31%, 31.8%, 20.8%, and 19% higher than those of the lightweight networks YOLOv4_tiny, Eifficientdet-d0, YOLOv5n, and YOLOv5s, respectively, and recall and F1 values are only 2.36% and 1.4% lower than those of YOLOv4.