92 research outputs found
Accurate TOA-Based UWB Localization System in Coal Mine Based on WSN
AbstractOver the last years, there has been a great deal of interest in Ultra Wideband (UWB) wireless communication and Wireless Sensor Networks(WSN), especially following the proposing of the internet of things by the MIT (Massachusetts Institute of Technology) in 1999, hich is also result in an increasing research on UWB and WSN applications. This article mainly introduced the accurate UWB Localization System based on WSN in coal mine. Firstly, we briefly introduced UWB and WSN Localization technology. Secondly, the advantages and disadvantages of the previous personnel localization technology in coal mine was analyzed and contrasted, and then the suitable personnel localization system in coal mine based on UWB signal and TOA estimate positioning scheme are presented. At last the rationality and feasibility of this scheme was proved through the simulation results
OVSNet : Towards One-Pass Real-Time Video Object Segmentation
Video object segmentation aims at accurately segmenting the target object
regions across consecutive frames. It is technically challenging for coping
with complicated factors (e.g., shape deformations, occlusion and out of the
lens). Recent approaches have largely solved them by using backforth
re-identification and bi-directional mask propagation. However, their methods
are extremely slow and only support offline inference, which in principle
cannot be applied in real time. Motivated by this observation, we propose a
efficient detection-based paradigm for video object segmentation. We propose an
unified One-Pass Video Segmentation framework (OVS-Net) for modeling
spatial-temporal representation in a unified pipeline, which seamlessly
integrates object detection, object segmentation, and object re-identification.
The proposed framework lends itself to one-pass inference that effectively and
efficiently performs video object segmentation. Moreover, we propose a
maskguided attention module for modeling the multi-scale object boundary and
multi-level feature fusion. Experiments on the challenging DAVIS 2017
demonstrate the effectiveness of the proposed framework with comparable
performance to the state-of-the-art, and the great efficiency about 11.5 FPS
towards pioneering real-time work to our knowledge, more than 5 times faster
than other state-of-the-art methods.Comment: 10 pages, 6 figure
Local-to-Global Information Communication for Real-Time Semantic Segmentation Network Search
Neural Architecture Search (NAS) has shown great potentials in automatically
designing neural network architectures for real-time semantic segmentation.
Unlike previous works that utilize a simplified search space with cell-sharing
way, we introduce a new search space where a lightweight model can be more
effectively searched by replacing the cell-sharing manner with cell-independent
one. Based on this, the communication of local to global information is
achieved through two well-designed modules. For local information exchange, a
graph convolutional network (GCN) guided module is seamlessly integrated as a
communication deliver between cells. For global information aggregation, we
propose a novel dense-connected fusion module (cell) which aggregates
long-range multi-level features in the network automatically. In addition, a
latency-oriented constraint is endowed into the search process to balance the
accuracy and latency. We name the proposed framework as Local-to-Global
Information Communication Network Search (LGCNet). Extensive experiments on
Cityscapes and CamVid datasets demonstrate that LGCNet achieves the new
state-of-the-art trade-off between accuracy and speed. In particular, on
Cityscapes dataset, LGCNet achieves the new best performance of 74.0\% mIoU
with the speed of 115.2 FPS on Titan Xp.Comment: arXiv admin note: text overlap with arXiv:1909.0679
Search What You Want: Barrier Panelty NAS for Mixed Precision Quantization
Emergent hardwares can support mixed precision CNN models inference that
assign different bitwidths for different layers. Learning to find an optimal
mixed precision model that can preserve accuracy and satisfy the specific
constraints on model size and computation is extremely challenge due to the
difficult in training a mixed precision model and the huge space of all
possible bit quantizations. In this paper, we propose a novel soft Barrier
Penalty based NAS (BP-NAS) for mixed precision quantization, which ensures all
the searched models are inside the valid domain defined by the complexity
constraint, thus could return an optimal model under the given constraint by
conducting search only one time. The proposed soft Barrier Penalty is
differentiable and can impose very large losses to those models outside the
valid domain while almost no punishment for models inside the valid domain,
thus constraining the search only in the feasible domain. In addition, a
differentiable Prob-1 regularizer is proposed to ensure learning with NAS is
reasonable. A distribution reshaping training strategy is also used to make
training more stable. BP-NAS sets new state of the arts on both classification
(Cifar-10, ImageNet) and detection (COCO), surpassing all the efficient mixed
precision methods designed manually and automatically. Particularly, BP-NAS
achieves higher mAP (up to 2.7\% mAP improvement) together with lower bit
computation cost compared with the existing best mixed precision model on COCO
detection.Comment: ECCV202
Graph-guided Architecture Search for Real-time Semantic Segmentation
Designing a lightweight semantic segmentation network often requires
researchers to find a trade-off between performance and speed, which is always
empirical due to the limited interpretability of neural networks. In order to
release researchers from these tedious mechanical trials, we propose a
Graph-guided Architecture Search (GAS) pipeline to automatically search
real-time semantic segmentation networks. Unlike previous works that use a
simplified search space and stack a repeatable cell to form a network, we
introduce a novel search mechanism with new search space where a lightweight
model can be effectively explored through the cell-level diversity and
latencyoriented constraint. Specifically, to produce the cell-level diversity,
the cell-sharing constraint is eliminated through the cell-independent manner.
Then a graph convolution network (GCN) is seamlessly integrated as a
communication mechanism between cells. Finally, a latency-oriented constraint
is endowed into the search process to balance the speed and performance.
Extensive experiments on Cityscapes and CamVid datasets demonstrate that GAS
achieves the new state-of-the-art trade-off between accuracy and speed. In
particular, on Cityscapes dataset, GAS achieves the new best performance of
73.5% mIoU with speed of 108.4 FPS on Titan Xp.Comment: CVPR202
Uncertainty-Aware Consistency Regularization for Cross-Domain Semantic Segmentation
Unsupervised domain adaptation (UDA) aims to adapt existing models of the
source domain to a new target domain with only unlabeled data. Many
adversarial-based UDA methods involve high-instability training and have to
carefully tune the optimization procedure. Some non-adversarial UDA methods
employ a consistency regularization on the target predictions of a student
model and a teacher model under different perturbations, where the teacher
shares the same architecture with the student and is updated by the exponential
moving average of the student. However, these methods suffer from noticeable
negative transfer resulting from either the error-prone discriminator network
or the unreasonable teacher model. In this paper, we propose an
uncertainty-aware consistency regularization method for cross-domain semantic
segmentation. By exploiting the latent uncertainty information of the target
samples, more meaningful and reliable knowledge from the teacher model can be
transferred to the student model. In addition, we further reveal the reason why
the current consistency regularization is often unstable in minimizing the
distribution discrepancy. We also show that our method can effectively ease
this issue by mining the most reliable and meaningful samples with a dynamic
weighting scheme of consistency loss. Experiments demonstrate that the proposed
method outperforms the state-of-the-art methods on two domain adaptation
benchmarks, GTAV Cityscapes and SYNTHIA
Cityscapes
SFNet: Faster and Accurate Semantic Segmentation via Semantic Flow
In this paper, we focus on exploring effective methods for faster and
accurate semantic segmentation. A common practice to improve the performance is
to attain high-resolution feature maps with strong semantic representation. Two
strategies are widely used: atrous convolutions and feature pyramid fusion,
while both are either computationally intensive or ineffective. Inspired by the
Optical Flow for motion alignment between adjacent video frames, we propose a
Flow Alignment Module (FAM) to learn \textit{Semantic Flow} between feature
maps of adjacent levels and broadcast high-level features to high-resolution
features effectively and efficiently. Furthermore, integrating our FAM to a
standard feature pyramid structure exhibits superior performance over other
real-time methods, even on lightweight backbone networks, such as ResNet-18 and
DFNet. Then to further speed up the inference procedure, we also present a
novel Gated Dual Flow Alignment Module to directly align high-resolution
feature maps and low-resolution feature maps where we term the improved version
network as SFNet-Lite. Extensive experiments are conducted on several
challenging datasets, where results show the effectiveness of both SFNet and
SFNet-Lite. In particular, when using Cityscapes test set, the SFNet-Lite
series achieve 80.1 mIoU while running at 60 FPS using ResNet-18 backbone and
78.8 mIoU while running at 120 FPS using STDC backbone on RTX-3090. Moreover,
we unify four challenging driving datasets into one large dataset, which we
named Unified Driving Segmentation (UDS) dataset. It contains diverse domain
and style information. We benchmark several representative works on UDS. Both
SFNet and SFNet-Lite still achieve the best speed and accuracy trade-off on
UDS, which serves as a strong baseline in such a challenging setting. The code
and models are publicly available at https://github.com/lxtGH/SFSegNets.Comment: IJCV-2023; Extension of Previous work arXiv:2002.1012
Betrayed by Captions: Joint Caption Grounding and Generation for Open Vocabulary Instance Segmentation
In this work, we focus on open vocabulary instance segmentation to expand a
segmentation model to classify and segment instance-level novel categories.
Previous approaches have relied on massive caption datasets and complex
pipelines to establish one-to-one mappings between image regions and words in
captions. However, such methods build noisy supervision by matching non-visible
words to image regions, such as adjectives and verbs. Meanwhile, context words
are also important for inferring the existence of novel objects as they show
high inter-correlations with novel categories. To overcome these limitations,
we devise a joint \textbf{Caption Grounding and Generation (CGG)} framework,
which incorporates a novel grounding loss that only focuses on matching object
nouns to improve learning efficiency. We also introduce a caption generation
head that enables additional supervision and contextual modeling as a
complementation to the grounding loss. Our analysis and results demonstrate
that grounding and generation components complement each other, significantly
enhancing the segmentation performance for novel classes. Experiments on the
COCO dataset with two settings: Open Vocabulary Instance Segmentation (OVIS)
and Open Set Panoptic Segmentation (OSPS) demonstrate the superiority of the
CGG. Specifically, CGG achieves a substantial improvement of 6.8% mAP for novel
classes without extra data on the OVIS task and 15% PQ improvements for novel
classes on the OSPS benchmark.Comment: ICCV-202
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