Applying Surface Normal Information in Drivable Area and Road Anomaly Detection for Ground Mobile Robots

• URL: http://arxiv.org/abs/2008.11383v1
• Date: Wed, 26 Aug 2020 05:44:07 GMT
• Title: Applying Surface Normal Information in Drivable Area and Road Anomaly Detection for Ground Mobile Robots
• Authors: Hengli Wang, Rui Fan, Yuxiang Sun, Ming Liu
• Abstract summary: We develop a novel module named the Normal Inference Module (NIM), which can generate surface normal information from dense depth images with high accuracy and efficiency. Our NIM can be deployed in existing convolutional neural networks (CNNs) to refine the segmentation performance. Our proposed NIM-RTFNet ranks 8th on the KITTI road benchmark and exhibits a real-time inference speed.
• Score: 29.285200656398562
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The joint detection of drivable areas and road anomalies is a crucial task for ground mobile robots. In recent years, many impressive semantic segmentation networks, which can be used for pixel-level drivable area and road anomaly detection, have been developed. However, the detection accuracy still needs improvement. Therefore, we develop a novel module named the Normal Inference Module (NIM), which can generate surface normal information from dense depth images with high accuracy and efficiency. Our NIM can be deployed in existing convolutional neural networks (CNNs) to refine the segmentation performance. To evaluate the effectiveness and robustness of our NIM, we embed it in twelve state-of-the-art CNNs. The experimental results illustrate that our NIM can greatly improve the performance of the CNNs for drivable area and road anomaly detection. Furthermore, our proposed NIM-RTFNet ranks 8th on the KITTI road benchmark and exhibits a real-time inference speed.

Related papers

• PNAS-MOT: Multi-Modal Object Tracking with Pareto Neural Architecture Search [64.28335667655129]
  Multiple object tracking is a critical task in autonomous driving. As tracking accuracy improves, neural networks become increasingly complex, posing challenges for their practical application in real driving scenarios due to the high level of latency. In this paper, we explore the use of the neural architecture search (NAS) methods to search for efficient architectures for tracking, aiming for low real-time latency while maintaining relatively high accuracy.
  arXiv  Detail & Related papers  (2024-03-23T04:18:49Z)
• SFOD: Spiking Fusion Object Detector [10.888008544975662]
  Spiking Fusion Object Detector (SFOD) is a simple and efficient approach to SNN-based object detection. We design a Spiking Fusion Module, achieving the first-time fusion of feature maps from different scales in SNNs applied to event cameras. We establish state-of-the-art classification results based on SNNs, achieving 93.7% accuracy on the NCAR dataset.
  arXiv  Detail & Related papers  (2024-03-22T13:24:50Z)
• Deep Multi-Threshold Spiking-UNet for Image Processing [51.88730892920031]
  This paper introduces the novel concept of Spiking-UNet for image processing, which combines the power of Spiking Neural Networks (SNNs) with the U-Net architecture. To achieve an efficient Spiking-UNet, we face two primary challenges: ensuring high-fidelity information propagation through the network via spikes and formulating an effective training strategy. Experimental results show that, on image segmentation and denoising, our Spiking-UNet achieves comparable performance to its non-spiking counterpart.
  arXiv  Detail & Related papers  (2023-07-20T16:00:19Z)
• Object Detection with Spiking Neural Networks on Automotive Event Data [0.0]
  We propose to train spiking neural networks (SNNs) directly on data coming from event cameras to design fast and efficient automotive embedded applications. In this paper, we conducted experiments on two automotive event datasets, establishing new state-of-the-art classification results for spiking neural networks.
  arXiv  Detail & Related papers  (2022-05-09T14:39:47Z)
• Automatic Extraction of Road Networks from Satellite Images by using Adaptive Structural Deep Belief Network [0.0]
  Our model is applied to an automatic recognition method of road network system, called RoadTracer. RoadTracer can generate a road map on the ground surface from aerial photograph data. In order to improve the accuracy and the calculation time, our Adaptive DBN was implemented on the RoadTracer instead of the CNN.
  arXiv  Detail & Related papers  (2021-10-25T07:06:10Z)
• Dynamic Fusion Module Evolves Drivable Area and Road Anomaly Detection: A Benchmark and Algorithms [16.417299198546168]
  Joint detection of drivable areas and road anomalies is very important for mobile robots. In this paper, we first build a drivable area and road anomaly detection benchmark for ground mobile robots. We propose a novel module, referred to as the dynamic fusion module (DFM), which can be easily deployed in existing data-fusion networks.
  arXiv  Detail & Related papers  (2021-03-03T14:38:27Z)
• GraN: An Efficient Gradient-Norm Based Detector for Adversarial and Misclassified Examples [77.99182201815763]
  Deep neural networks (DNNs) are vulnerable to adversarial examples and other data perturbations. GraN is a time- and parameter-efficient method that is easily adaptable to any DNN. GraN achieves state-of-the-art performance on numerous problem set-ups.
  arXiv  Detail & Related papers  (2020-04-20T10:09:27Z)
• HyNNA: Improved Performance for Neuromorphic Vision Sensor based Surveillance using Hybrid Neural Network Architecture [7.293414498855147]
  We improve on a recently proposed hybrid event-frame approach by using morphological image processing algorithms for region proposal. We also address the low-power requirement for object detection and classification by exploring various convolutional neural network (CNN) architectures. Specifically, we compare the results obtained from our object detection framework against the state-of-the-art low-power NVS surveillance system and show an improved accuracy of 82.16% from 63.1%.
  arXiv  Detail & Related papers  (2020-03-19T07:18:33Z)
• 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)
• Temporal Pulses Driven Spiking Neural Network for Fast Object Recognition in Autonomous Driving [65.36115045035903]
  We propose an approach to address the object recognition problem directly with raw temporal pulses utilizing the spiking neural network (SNN) Being evaluated on various datasets, our proposed method has shown comparable performance as the state-of-the-art methods, while achieving remarkable time efficiency.
  arXiv  Detail & Related papers  (2020-01-24T22:58:55Z)
• Deep Learning based Pedestrian Inertial Navigation: Methods, Dataset and On-Device Inference [49.88536971774444]
  Inertial measurements units (IMUs) are small, cheap, energy efficient, and widely employed in smart devices and mobile robots. Exploiting inertial data for accurate and reliable pedestrian navigation supports is a key component for emerging Internet-of-Things applications and services. We present and release the Oxford Inertial Odometry dataset (OxIOD), a first-of-its-kind public dataset for deep learning based inertial navigation research.
  arXiv  Detail & Related papers  (2020-01-13T04:41:54Z)

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.