TY - JOUR A2 - Oliva, Diego AU - Sellat, Qusay AU - Bisoy, SukantKishoro AU - Priyadarshini, Rojalina AU - Vidyarthi, Ankit AU - Kautish, Sandeep AU - Barik, Rabindra K. PY - 2022 DA - 2022/01/17 TI - Intelligent Semantic Segmentation for Self-Driving Vehicles Using Deep Learning SP - 6390260 VL - 2022 AB - Understanding the situation is a critical component of any self-driving system. Accurate real-time visual signal processing to create pixelwise classed pictures, also known as semantic segmentation, is critical for scenario comprehension and subsequent acceptance of this new technology. Due to the intricate interaction between pixels in each frame of the received camera data, such efficiency in terms of processing time and accuracy could not be achieved prior to recent advances in deep learning algorithms. We present an effective approach for semantic segmentation for self-driving automobiles in this study. We combine deep learning architectures like convolutional neural networks and autoencoders, as well as cutting-edge approaches like feature pyramid networks and bottleneck residual blocks, to develop our model. The CamVid dataset, which has undergone considerable data augmentation, is utilised to train and test our model. To validate the suggested model, we compare the acquired findings to various baseline models reported in the literature. SN - 1687-5265 UR - https://doi.org/10.1155/2022/6390260 DO - 10.1155/2022/6390260 JF - Computational Intelligence and Neuroscience PB - Hindawi KW - ER -