Efficient fine-grained road segmentation using superpixel-based CNN and CRF models

• URL: http://arxiv.org/abs/2207.02844v1
• Date: Wed, 22 Jun 2022 12:38:30 GMT
• Title: Efficient fine-grained road segmentation using superpixel-based CNN and CRF models
• Authors: Farnoush Zohourian, Jan Siegemund, Mirko Meuter, Josef Pauli
• Abstract summary: We propose a novel approach to utilise the advantages of CNNs for the task of road segmentation at reasonable computational effort. The proposed system obtained comparable performance among the top performing algorithms on the KITTI road benchmark.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Towards a safe and comfortable driving, road scene segmentation is a rudimentary problem in camera-based advance driver assistance systems (ADAS). Despite of the great achievement of Convolutional Neural Networks (CNN) for semantic segmentation task, the high computational efforts of CNN based methods is still a challenging area. In recent work, we proposed a novel approach to utilise the advantages of CNNs for the task of road segmentation at reasonable computational effort. The runtime benefits from using irregular super pixels as basis for the input for the CNN rather than the image grid, which tremendously reduces the input size. Although, this method achieved remarkable low computational time in both training and testing phases, the lower resolution of the super pixel domain yields naturally lower accuracy compared to high cost state of the art methods. In this work, we focus on a refinement of the road segmentation utilising a Conditional Random Field (CRF).The refinement procedure is limited to the super pixels touching the predicted road boundary to keep the additional computational effort low. Reducing the input to the super pixel domain allows the CNNs structure to stay small and efficient to compute while keeping the advantage of convolutional layers and makes them eligible for ADAS. Applying CRF compensate the trade off between accuracy and computational efficiency. The proposed system obtained comparable performance among the top performing algorithms on the KITTI road benchmark and its fast inference makes it particularly suitable for realtime applications.

Related papers

• LeRF: Learning Resampling Function for Adaptive and Efficient Image Interpolation [64.34935748707673]
  Recent deep neural networks (DNNs) have made impressive progress in performance by introducing learned data priors. We propose a novel method of Learning Resampling (termed LeRF) which takes advantage of both the structural priors learned by DNNs and the locally continuous assumption. LeRF assigns spatially varying resampling functions to input image pixels and learns to predict the shapes of these resampling functions with a neural network.
  arXiv  Detail & Related papers  (2024-07-13T16:09:45Z)
• Progressive Minimal Path Method with Embedded CNN [2.4975981795360847]
  We propose Path-CNN, a method for the segmentation of centerlines of tubular structures by embedding convolutional neural networks (CNNs) into the progressive minimal path method. Our method has lower hardware requirements than many recent methods.
  arXiv  Detail & Related papers  (2022-04-02T22:00:27Z)
• Learning from Images: Proactive Caching with Parallel Convolutional Neural Networks [94.85780721466816]
  A novel framework for proactive caching is proposed in this paper. It combines model-based optimization with data-driven techniques by transforming an optimization problem into a grayscale image. Numerical results show that the proposed scheme can reduce 71.6% computation time with only 0.8% additional performance cost.
  arXiv  Detail & Related papers  (2021-08-15T21:32:47Z)
• Semantic Segmentation by Improved Generative Adversarial Networks [0.0]
  We introduce Convolutional CRFs (ConvCRFs) as an effective improvement solution for the image semantic segmentation task. Our method not only learns an end-to-end mapping from input image to corresponding output image, but also learns a loss function to train this mapping.
  arXiv  Detail & Related papers  (2021-04-20T11:59:29Z)
• AINet: Association Implantation for Superpixel Segmentation [82.21559299694555]
  We propose a novel textbfAssociation textbfImplantation (AI) module to enable the network to explicitly capture the relations between the pixel and its surrounding grids. Our method could not only achieve state-of-the-art performance but maintain satisfactory inference efficiency.
  arXiv  Detail & Related papers  (2021-01-26T10:40:13Z)
• Multi-Task Network Pruning and Embedded Optimization for Real-time Deployment in ADAS [0.0]
  Camera-based Deep Learning algorithms are increasingly needed for perception in Automated Driving systems. constraints from the automotive industry challenge the deployment of CNNs by imposing embedded systems with limited computational resources. We propose an approach to embed a multi-task CNN network under such conditions on a commercial prototype platform.
  arXiv  Detail & Related papers  (2021-01-19T19:29:38Z)
• CNNs for JPEGs: A Study in Computational Cost [49.97673761305336]
  Convolutional neural networks (CNNs) have achieved astonishing advances over the past decade. CNNs are capable of learning robust representations of the data directly from the RGB pixels. Deep learning methods capable of learning directly from the compressed domain have been gaining attention in recent years.
  arXiv  Detail & Related papers  (2020-12-26T15:00:10Z)
• Adaptive Pixel-wise Structured Sparse Network for Efficient CNNs [11.377052459168942]
  This paper proposes a novel spatially adaptive framework that can generate pixel-wise sparsity according to the input image. A sparse controlling method is presented to enable online adjustment for applications with different precision/latency requirements.
  arXiv  Detail & Related papers  (2020-10-21T15:47:13Z)
• Real-time Semantic Segmentation with Fast Attention [94.88466483540692]
  We propose a novel architecture for semantic segmentation of high-resolution images and videos in real-time. The proposed architecture relies on our fast spatial attention, which is a simple yet efficient modification of the popular self-attention mechanism. We show that results on multiple datasets demonstrate superior performance with better accuracy and speed compared to existing approaches.
  arXiv  Detail & Related papers  (2020-07-07T22:37:16Z)
• Real-Time High-Performance Semantic Image Segmentation of Urban Street Scenes [98.65457534223539]
  We propose a real-time high-performance DCNN-based method for robust semantic segmentation of urban street scenes. The proposed method achieves the accuracy of 73.6% and 68.0% mean Intersection over Union (mIoU) with the inference speed of 51.0 fps and 39.3 fps.
  arXiv  Detail & Related papers  (2020-03-11T08:45:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.