الفهرس 
IntroductionOnly 14 pages are availabe for public view
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Abstract
The First step of autonomous car is based on visual scene understanding of the surrounding environment. This visual understanding entails identification and localization of surrounding objects. Developing a semantic image segmentation architecture, for segmenting the entire view into regions and assigning a semantic label to these regions, lies at the heart of this problem. This thesis proposes an e↵ective and e cient semantic image segmentation model for autonomous driving.
In the last several years, semantic image segmentation likes other computer vision tasks as object detection and image classification, has seen considerable advancements due to the employment of deep learning architectures, especially convolutional neural networks CNN. Training such architectures to obtain a high level of accuracy requires a very complex model. Being the autonomous driving a critical real-time application, computationally e cient models are needed. Also, edge devices as mobile phones have a low capacity of computational power. Also, this requires a specific and e cient deep neural networks. Although there are many ways to design deep neural networks and the availability of e cient training hardware, still designing a high accuracy and computationally e cient models is very challenging.
This thesis focuses on providing e cient deep neural networks for semantic image segmentation at two concurrent levels.
Computationally E cient Model: we designed lightweight neural networks for semantic segmentation by following up the encoder-decoder structure, employing lightweight e cient backbone networks, and designing lightweight e cient decoder module.
High Accuracy Model: while considering the computational cost of the proposed models in our mind, we also consider the performance e ciency in terms of accuracy by employing long and short residual connections and designing e cient module called Deeper Atrous Spatial Pyramid Pooling (DASPP) to capture the extracted features by the encoder section at multi-level context.
We evaluated our model on the standard dataset Cityscapes. Also, in our evaluation procedure, we evaluated our model on severe weather condition on the standard dataset Foggy Cityscapes. A three variant of semantic segmentations model are proposed to provide multiple trade-o↵s between accuracy and computational e ciency. Our model LiteSeg-Mobilenet can achieve 161 frame per second (FPS) with mean intersection over union (mIOU) of 67.81% while the previous state-of-the-art ESPNet on the same hardware can achieve 144 FPS with mIOU of 60.3% on the standard Cityscapes test set.