Efficient Semantic Segmentation on Edge Devices

• URL: http://arxiv.org/abs/2212.13691v1
• Date: Wed, 28 Dec 2022 04:13:11 GMT
• Title: Efficient Semantic Segmentation on Edge Devices
• Authors: Farshad Safavi, Irfan Ali, Venkatesh Dasari, Guanqun Song, Ting Zhu
• Abstract summary: This project analyzes current semantic segmentation models to explore the feasibility of applying these models for emergency response during catastrophic events. We compare the performance of real-time semantic segmentation models with non-real-time counterparts constrained by aerial images under oppositional settings. Furthermore, we train several models on the Flood-Net dataset, containing UAV images captured after Hurricane Harvey, and benchmark their execution on special classes such as flooded buildings vs. non-flooded buildings or flooded roads vs. non-flooded roads.
• Score: 7.5562201794440185
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semantic segmentation works on the computer vision algorithm for assigning each pixel of an image into a class. The task of semantic segmentation should be performed with both accuracy and efficiency. Most of the existing deep FCNs yield to heavy computations and these networks are very power hungry, unsuitable for real-time applications on portable devices. This project analyzes current semantic segmentation models to explore the feasibility of applying these models for emergency response during catastrophic events. We compare the performance of real-time semantic segmentation models with non-real-time counterparts constrained by aerial images under oppositional settings. Furthermore, we train several models on the Flood-Net dataset, containing UAV images captured after Hurricane Harvey, and benchmark their execution on special classes such as flooded buildings vs. non-flooded buildings or flooded roads vs. non-flooded roads. In this project, we developed a real-time UNet based model and deployed that network on Jetson AGX Xavier module.

Related papers

• SIGMA:Sinkhorn-Guided Masked Video Modeling [69.31715194419091]
  Sinkhorn-guided Masked Video Modelling ( SIGMA) is a novel video pretraining method. We distribute features of space-time tubes evenly across a limited number of learnable clusters. Experimental results on ten datasets validate the effectiveness of SIGMA in learning more performant, temporally-aware, and robust video representations.
  arXiv  Detail & Related papers  (2024-07-22T08:04:09Z)
• Filling Missing Values Matters for Range Image-Based Point Cloud Segmentation [12.62718910894575]
  Point cloud segmentation (PCS) plays an essential role in robot perception and navigation tasks. To efficiently understand large-scale outdoor point clouds, their range image representation is commonly adopted. However, undesirable missing values in the range images damage the shapes and patterns of objects. This problem creates difficulty for the models in learning coherent and complete geometric information from the objects.
  arXiv  Detail & Related papers  (2024-05-16T15:13:42Z)
• Lidar Annotation Is All You Need [0.0]
  This paper aims to improve the efficiency of image segmentation using a convolutional neural network in a multi-sensor setup. The key innovation of our approach is the masked loss, addressing sparse ground-truth masks from point clouds. Experimental validation of the approach on benchmark datasets shows comparable performance to a high-quality image segmentation model.
  arXiv  Detail & Related papers  (2023-11-08T15:55:18Z)
• Real-Time Semantic Segmentation using Hyperspectral Images for Mapping Unstructured and Unknown Environments [2.408714894793063]
  We propose the use of hyperspectral images for real-time pixel-wise semantic classification and segmentation. The resulting segmented image is processed to extract, filter, and approximate objects as polygons. The resulting polygons are then used to generate a semantic map of the environment.
  arXiv  Detail & Related papers  (2023-03-27T22:33:55Z)
• Location-Aware Self-Supervised Transformers [74.76585889813207]
  We propose to pretrain networks for semantic segmentation by predicting the relative location of image parts. We control the difficulty of the task by masking a subset of the reference patch features visible to those of the query. Our experiments show that this location-aware pretraining leads to representations that transfer competitively to several challenging semantic segmentation benchmarks.
  arXiv  Detail & Related papers  (2022-12-05T16:24:29Z)
• Deep Learning Computer Vision Algorithms for Real-time UAVs On-board Camera Image Processing [77.34726150561087]
  This paper describes how advanced deep learning based computer vision algorithms are applied to enable real-time on-board sensor processing for small UAVs. All algorithms have been developed using state-of-the-art image processing methods based on deep neural networks.
  arXiv  Detail & Related papers  (2022-11-02T11:10:42Z)
• Group-Wise Semantic Mining for Weakly Supervised Semantic Segmentation [49.90178055521207]
  This work addresses weakly supervised semantic segmentation (WSSS), with the goal of bridging the gap between image-level annotations and pixel-level segmentation. We formulate WSSS as a novel group-wise learning task that explicitly models semantic dependencies in a group of images to estimate more reliable pseudo ground-truths. In particular, we devise a graph neural network (GNN) for group-wise semantic mining, wherein input images are represented as graph nodes.
  arXiv  Detail & Related papers  (2020-12-09T12:40:13Z)
• End-to-End Trainable Deep Active Contour Models for Automated Image Segmentation: Delineating Buildings in Aerial Imagery [12.442780294349049]
  Trainable Deep Active Contours (TDACs) is an automatic image segmentation framework that unites Convolutional Networks (CNNs) and Active Contour Models (ACMs) TDAC yields fast, accurate, and fully automatic simultaneous delineation of arbitrarily many buildings in the image.
  arXiv  Detail & Related papers  (2020-07-22T21:27:17Z)
• CRNet: Cross-Reference Networks for Few-Shot Segmentation [59.85183776573642]
  Few-shot segmentation aims to learn a segmentation model that can be generalized to novel classes with only a few training images. With a cross-reference mechanism, our network can better find the co-occurrent objects in the two images. Experiments on the PASCAL VOC 2012 dataset show that our network achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2020-03-24T04:55:43Z)
• 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.