## 2 Background  
The method based on deep learning is based on the traditional neural network. It mainly extracts features through convolutional neural networks (CNNs) and classifies the pixels in the image area. It can extract more abstract image features. Through the training of a large amount of datasets, the model parameters are iteratively optimized, which can achieve higher road detection accuracy. However, due to the large processing data, higher requirements are placed on the computing hardware.  
The AlexNet network [6] improves the local response normalization (LRN) layer of the generalization ability of the model, expands the data through data augmentation, and modifies the network structure to achieve random dropout to prevent the model from overfitting. [PERSON] [7] et al. proposed to extract image features through CNN, combined with texture description factors to detect road driving areas in real time. [PERSON] [8] et al. from the University of California, Berkeley, proposed the idea of fully convolutional networks (FCNs), which extends the original CNN structure and decomposes the semantic segmentation task into a combination of encoder and decoder for the first time. [PERSON] et al. [9] proposed a road detection algorithm based on a fully convolutional neural network, which transforms the road detection problem into a two-class image semantic segmentation problem and introduces image context information, which has excellent performance on the KITTI road detection dataset and once achieved a road detection performance ahead of other algorithms. PSPNet [10] and Deeplab [3] use a spatial pyramid model to combine multi-scale contextual information, while [PERSON] [11], [PERSON] [12], and [PERSON] [13] et al. use dense layers at the decoder side to inject the advantage of multi-level feature reuse, and [PERSON] [14] et al. propose a recurrent GAN-based image enhancement network (IEC-Net) to enhance diverse features of the input image. Some of the recent studies have used attentional mechanisms to capture richer global contextual information [15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30]. YOLACT [4], D3 NET [5], and CycleGAN [14] network structures are proposed for the detection of drivable areas and to achieve good results. Although the effectiveness of these approaches has been validated on datasets collected from structured environments(e.g., Cityscapes [31] and ADE20K [32]), their effectiveness in unstructured and complex environments (e.g., rural, wilderness, and harsh weather) has not been verified.  
In terms of enhancing the lightweight of semantic segmentation algorithms, many lightweight models have been proposed, such as MobileNetV1, MobileNetV2 [33; 34], ESPNet [35], Enet [36], LAANet [37], and CCC [38]; also, [PERSON] [39] performed real-time image semantic segmentation by improving the DeepLabV2 network model, which obtains real-time operation by significantly reducing the computational and parametric quantities in the network model, but at the same time, it also reduces the measurement accuracy.  
To summarize, there is currently a lack of neural network research that balances accuracy and real-time performance for the detection of drivable areas in complex unstructured environments.