# Lightweight Deep Learning for Road Environment Recognition
## 4 Experimental Results
### Experimental Result and Analysis
Compared with PSPNet-ResNet50, DeepLabv3+, and UNet-ResNet50, which are high-accuracy models, it not only improves the mPA by 2.9%, 4%, and 2.6%, respectively, but also reduces the volume by 73.4%, 70%, and 71.9%, and it can be seen by the FPS that the processing speed of our proposed method is 3 times faster than these three high-accuracy models. This is due to our use of a lightweight backbone network, which greatly reduces the network size, and the addition of LRCA-Net in the network feature fusion part to improve the model performance. As can \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Input Size** & **Backbone** & **mAP** & **Parameters (Millions)** & **FPS** \\ \hline SSD & 300 \(\times\) 300 & VGG16 & 82.4 & 26.3 & 15 \\ \hline Faster & 600 \(\times\) 600 & VGG16 & 78.9 & 136.8 & 3 \\ RCNN-VGG16 & Faster & 600 \(\times\) 600 & ResNet50 & 84.1 & 28.4 & 13 \\ R-CNN-ResNet50 & & & & & \\ \hline YOLOX-Tiny & 640 \(\times\) 640 & Modified CSP & 85.2 & 5.1 & 72 \\ YOLOX-S & 640 \(\times\) 640 & Modified CSP & 87.7 & 8.9 & 41 \\ YOLOX-M & 640 \(\times\) 640 & Modified CSP & 89.3 & 25.3 & 14 \\ YOLOX-L & 640 \(\times\) 640 & Modified CSP & 92.6 & 54.2 & 6 \\ YOLOX-X & 640 \(\times\) 640 & Modified CSP & **93.7** & 99.1 & 3 \\ YOLOV4 & 416 \(\times\) 416 & CSPDark-53 & 90.7 & 64.4 & 5 \\ \hline EfficientDet-D0 & 512 \(\times\) 512 & Efficient-B0 & 76.9 & 3.9 & 96 \\ EfficientDet-D1 & 640 \(\times\) 640 & Efficient-B1 & 83.3 & 6.6 & 70 \\ EfficientDet-D2 & 768 \(\times\) 768 & Efficient-B2 & 89.5 & 8.1 & 42 \\ EfficientDet-D3 & 896 \(\times\) 896 & Efficient-B3 & 90.9 & 12 & 30 \\ EfficientDet-D4 & 1024 \(\times\) 1024 & Efficient-B4 & 91.5 & 20.7 & 18 \\ EfficientDet-D5 & 1280 \(\times\) 1280 & Efficient-B5 & 92.8 & 33.7 & 11 \\ \hline Our Approach-1 & 416 \(\times\) 416 & MobileNetv1 & 93.2 & 12.8 & 29 \\ Our Approach-2 & 416 \(\times\) 416 & MobileNetv2 & 92 & 10.9 & 34 \\ Our Approach-3 & 416 \(\times\) 416 & MobileNetv3 & 91.8 & 11.8 & 32 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of the VOC on the Pascal VOC 2007 and 2012 testing set, where bold numbers indicate the highest mAP in the column. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Backbone**} & \multicolumn{2}{c}{**PA**} & \multirow{2}{*}{**mPA**} & \multirow{2}{*}{**Parameters** (Millions)**} \\ \cline{3-4} \cline{6-6}  & & **Lane** & & **Background** & & **mPA** \\ \hline \multirow{3}{*}{CBAM} & MobileNetv1 & 93.3 & 89.5 & 91.4 & 12.6 \\  & MobileNetv2 & 91.5 & 90.1 & 90.8 & 10.8 \\  & MobileNetv3 & 93.7 & 90.5 & 92.1 & 11.8 \\ \hline \multirow{3}{*}{LRCA-Net} & MobileNetv1 & 95.5 & 90.7 & 93.1 & 12.3 \\  & MobileNetv2 & 92.9 & 91.1 & 92 & 10.5 \\ \cline{1-1}  & MobileNetv3 & 95.1 & 90.3 & 92.7 & 11.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._be seen in Table 8, our method produced the IoU 93.7% for lane lines and produces higher quality compared to other methods. Figure 16: Model Size vs. mAP. Details are in Table 6.  
Figure 17: Object detection results of the proposed model in real situations. (**a**) detection results of various vehicles on the road, (**b**) detection results of pedestrians and bicycles on the road, and (**c**) detection results of motorcycles and surrounding vehicles while the vehicle is moving. Figure 18: Model Size vs. mPA. Details are in Table 7.