## 4 Dataset
### Experiment of Drivable Area Detection Algorithm Based on Lightweight Neural Network  
In this paper, three datasets were constructed: the global drivable area dataset, the road drivable area dataset, and the off-road environment drivable area dataset. These datasets are utilized for training and testing the network model separately. Additionally, a self-built small dataset is employed as an auxiliary validation set. Its purpose is to verify if the model can converge and demonstrate a certain level of generalizability. Simultaneously, this validation set helps assess the impact of different datasets on the network's fitting ability. After the validation process, the final formal training is performed. The parameters are as follows: initial learning rate is set to 0.0001, decay ratio is 0.94, and the Adam optimizer is used; input image size is (3,469,469), automatic adjustment of the input image size is made through the internal interface, and the size of the network kernel parameters remains unchanged; batch size is 8, initial iteration number is 100, and single iteration data loading \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Vehicle Model** & **Body Size** & **Curb Weight** & **Power Type** \\ \hline Qichen R50 & 4280 \(\times\) 1695 \(\times\) 1535 & 1113 & Gasoline engine \\ MINIEV & 2920 \(\times\) 1493 \(\times\) 1621 & 700 & Pure electric \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental vehicle parameters. Figure 8: Industrial camera and lens. Figure 9: Experimental vehicles. Figure 11 shows the validation effect of the global training model, labeled with the real value for the visualization of the modified mask file. The modified global drivable area includes asphalt road, dirt ground, stone ground, and sand ground. The elements of obstacles and other attributes are grouped into the background class. The area covered by the red mask in the validation effect figure is the drivable area identified by the network model. Figure 10a shows that the first 10 iterations of the network converge faster. Before reaching 70 iterations, due to the small change in the loss function of successive generations, the training is terminated in advance, and the final verification set loss function is stable at about 0.083. It is shown that the detection division of the drivable area is more stable, and the misclassification with the background area is smaller. Figure 12: Local training model verification effect: (**a**) label the real value, (**b**) original images, and (**c**) local training model output. Figure 13: Cross-country training model verification effect: (**a**) cross-country training model validation results and (**b**) model loss function graph. Figure 12 is the verification effect diagram using the local training model. The dataset is labeled for the differentiation of lane drivable areas on structured roads. The mask files provide labeled visualizations of real values for the current lane, and both lanes are divided into two types of areas: the current drivable area and the optional drivable area. Lane lines are not labeled; in the environment where lane lines cannot be identified, the road area will be considered the current drivable area. The area covered by the red mask in Figure 12a is the current drivable area identified by the network model, and the area covered by the green mask is the optional drivable area identified. It is shown that the trained network model maintains a good level of detection for structured roads, with a small difference from the real value, and the unlabeled part of the validation set also achieves the detection of roads in drivable areas. From Figure 10b, it is shown that the model successfully completes the training convergence. It is shown that the network model in this paper is backwards compatible with the lane structure detection of structured roads when trained with a suitable dataset.