Detection of lane region under the road boundary is an imperative module for intelligent vehicle system. Lane markings provide separate regions on the road for the vehicles to avoid the possibility of accidents. Existing methods in lane detection have limited performance using various sensor-based approaches such as Radar and LiDAR and have high operational costs. To achieve a steady and optimal lane detection, the vision-based lane region detection scheme VLDNet is proposed which utilizes a encoder-decoder network using semantic segmentation architecture. In this direction, a hybrid model using UNet and ResNet has been adopted, where UNet is used as a segmentation model and ResNet-50 is used for down-sampling the image and identifying the required features. These identified features have been then applied into UNet for up-sampling and decoding the segments of the images. The publicly available KITTI dataset have been accessed for experiments and validation of the proposed network. The method outperforms the existing state-of-the-art methods in lane region detection. The network achieves better performance using standard evaluation measures such as accuracy of 98.87%, the precision of 98.24%, recall of 96.55%, frequency weighted IoU of 97.78%, and MaxF score of 97.77%.

道路边界下车道区域的检测是智能车辆系统必不可少的模块。车道标记在道路上为车辆提供了单独的区域，以避免发生事故的可能性。现有的车道检测方法使用各种基于传感器的方法（如雷达和激光雷达）性能有限，并且运营成本高。为了实现稳定和最佳的车道检测，提出了基于视觉的车道区域检测方案 VLDNet，该方案利用使用语义分割架构的编码器-解码器网络。在这个方向上，采用了使用 UNet 和 ResNet 的混合模型，其中使用 UNet 作为分割模型，使用 ResNet-50 对图像进行下采样并识别所需的特征。然后将这些识别出的特征应用于 UNet 以对图像的片段进行上采样和解码。已访问公开可用的 KITTI 数据集以进行拟议网络的实验和验证。该方法在车道区域检测方面优于现有的最先进方法。该网络使用标准评估指标实现了更好的性能，例如准确率 98.87%、精确度 98.24%、召回率 96.55%、频率加权 IoU 97.78% 和 MaxF 得分 97.77%。