## 1 Introduction  
Semantic segmentation plays a crucial role in scene interpretation and inferring relationships between different objects in autonomous vehicles. Instead of just identifying the labels of each pixel in an image, semantic segmentation facilitates in pinpointing the target as well as its boundary information. In recent days, there has been active research in semantic pixel-wise labeling. However, encoder-decoder-based neural networks have gained considerable attention for semantic pixel wise segmentation that predicts the class labels for each input image pixel [1]. An encoder usually constitutes a pre-trained Classification Network such as VGG [2], ResNet [3], MobileNet [4], etc. It uses the convolution layers with filter banks for down-sampling the image and generates high-dimensional feature maps for the input image. However, the down-sampling technique significantly reduces the essential spatial information for semantic segmentation. In order to overcome this limitation, decoders are designed to up-sample the obtained lower-resolution feature maps to their original size. These restored features offer pixel-level labels with refined object boundaries for segmentation. The generalized framework of the encoder-decoder-based segmentation model is depicted in Figure 1.  
Despite the different neural networks used for semantic segmentation, locating multiple objects in a single image is still a significant threat. In order to obtain the high-dimensional features of the input image for localization and to recover the size of the output image with that of the input, encoder-decoder-based segmentation models are considered in this work to perform pixel-level semantic segmentation on Indian roads for scene understanding [5].  
U-net is one of the primary encoder-decoder-based segmentation networks. Although it was initially developed for medical images, the U-net is currently being implemented in various applications, such as self-driving vehicles and remote sensing [6]. In 2015 U-net was proposed with two paths: contraction and expansion [7]. The novelty of this network lies in the expansive path, where the feature map is up-sampled after each stage, followed by concatenation and convolution. It also uses the skip connection to improve the segmentation result by generating a U-shaped network that keeps the original input image size as output. In 2017, a variant of U-net named LinkNet was proposed by [PERSON] and [PERSON] [8], which replaces the convolution block in the encoder with a residual block, and uses the \"addition\" operation feature instead of \"stacking\". It consists of four encoder and decoder blocks. Batch normalization is used after each convolution, followed by a ReLU activation function. The novelty lies in bypassing the output after each down-sampling step from each encoder block to its corresponding decoder block. In the same year, 2017, [PERSON] proposed the PSPNet (Pyramid Scene Parsing Network) [9]. The encoder in this network contains a CNN as a backbone, along with a dilated convolution layer and pyramid pooling modules. The initial feature map obtained from the backbone has additional features due to the dilated convolution layer. The decoder follows a similar structure as U-net, with convolution followed by a bi-linear up-sampling layer. Later, FPN (Feature Pyramid Network), the extended version of PSPNet, was introduced in 2019 for object detection [10]. It works on any image size and calculates the feature map at each level, irrespective of the backbone network. In the encoder stage, the features are up-sampled and concatenated with the features in the decoder, so that the exact image size is maintained as input. In our work, a pre-trained classification network is used to modify the encoder blocks of all four segmentation models. In the upcoming session, we will present a detailed overview of the different segmentation models using modified backbone networks.