scholarly journals DSNet: an efficient CNN for road scene segmentation

Author(s):  
Ping-Rong Chen ◽  
Hsueh-Ming Hang ◽  
Sheng-Wei Chan ◽  
Jing-Jhih Lin

Road scene understanding is a critical component in an autonomous driving system. Although the deep learning-based road scene segmentation can achieve very high accuracy, its complexity is also very high for developing real-time applications. It is challenging to design a neural net with high accuracy and low computational complexity. To address this issue, we investigate the advantages and disadvantages of several popular convolutional neural network (CNN) architectures in terms of speed, storage, and segmentation accuracy. We start from the fully convolutional network with VGG, and then we study ResNet and DenseNet. Through detailed experiments, we pick up the favorable components from the existing architectures and at the end, we construct a light-weight network architecture based on the DenseNet. Our proposed network, called DSNet, demonstrates a real-time testing (inferencing) ability (on the popular GPU platform) and it maintains an accuracy comparable with most previous systems. We test our system on several datasets including the challenging Cityscapes dataset (resolution of 1024 × 512) with an Mean Intersection over Union (mIoU) of about 69.1% and runtime of 0.0147 s/image on a single GTX 1080Ti. We also design a more accurate model but at the price of a slower speed, which has an mIoU of about 72.6% on the CamVid dataset.

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8072
Author(s):  
Yu-Bang Chang ◽  
Chieh Tsai ◽  
Chang-Hong Lin ◽  
Poki Chen

As the techniques of autonomous driving become increasingly valued and universal, real-time semantic segmentation has become very popular and challenging in the field of deep learning and computer vision in recent years. However, in order to apply the deep learning model to edge devices accompanying sensors on vehicles, we need to design a structure that has the best trade-off between accuracy and inference time. In previous works, several methods sacrificed accuracy to obtain a faster inference time, while others aimed to find the best accuracy under the condition of real time. Nevertheless, the accuracies of previous real-time semantic segmentation methods still have a large gap compared to general semantic segmentation methods. As a result, we propose a network architecture based on a dual encoder and a self-attention mechanism. Compared with preceding works, we achieved a 78.6% mIoU with a speed of 39.4 FPS with a 1024 × 2048 resolution on a Cityscapes test submission.


2017 ◽  
Vol 38 (SI 2 - 6th Conf EFPP 2002) ◽  
pp. 406-407
Author(s):  
A.M. Hietala ◽  
M. Eikenes ◽  
M. Kvaalen ◽  
H. Solheim ◽  
C.G. Fossdal

A quantitative multiplex real-time PCR procedure was developed to monitor the dynamics in Norway spruce-Heterobasidion annosum pathosystem. The assay reliably detected down to 1 pg of H. annosum DNA and 1 ng of host DNA in multiplex conditions. As a comparative method for quantifying fungal colonization, we applied the ergosterol assay. There was a very high correlation between the results obtained with the two methods, this strengthening the credibility of both assays. The advantages and disadvantages of these assays are discussed.


2019 ◽  
Vol 4 (2) ◽  
pp. 57-62
Author(s):  
Julisa Bana Abraham

The convolutional neural network is commonly used for classification. However, convolutional networks can also be used for semantic segmentation using the fully convolutional network approach. U-Net is one example of a fully convolutional network architecture capable of producing accurate segmentation on biomedical images. This paper proposes to use U-Net for Plasmodium segmentation on thin blood smear images. The evaluation shows that U-Net can accurately perform Plasmodium segmentation on thin blood smear images, besides this study also compares the three loss functions, namely mean-squared error, binary cross-entropy, and Huber loss. The results show that Huber loss has the best testing metrics: 0.9297, 0.9715, 0.8957, 0.9096 for F1 score, positive predictive value (PPV), sensitivity (SE), and relative segmentation accuracy (RSA), respectively.


2020 ◽  
Author(s):  
Andrea Antonini ◽  
Alberto Ortolani ◽  
Aldo Sonnini ◽  
Massimo Viti ◽  
Luca Fibbi ◽  
...  

<p>Atmospheric events are driven by surface sea physical parameters, including the exchanges of water vapor with the overlying atmosphere. Oceans cover around 70 percent of the Earth's surface and influence the atmospheric circulation, causing some of the main weather events. The lack of surface observations over the vast ocean areas is a critical problem to be addressed for improving the performance of weather forecasting.</p><p>Even if weather observations over sea from ships have been collected for over 200 years and used for meteorological research and climate applications, only recently the availability of different telecommunication solutions make real time access to measurements possible, even from remote areas. This is consequently opening new opportunities to use data from marine areas in operational weather applications.</p><p>Ground based GNSS receivers has been used for many years to determine a quantity that is of major interest for meteorologists and climatologists, the water vapor content, derived from the Zenith Path Delay. GNSS meteorology has been also tested over ships during some measurement campaigns in the past.</p><p>This work presents the implementation of the first GNSS meteo infrastructure on ships operating on the northwestern Mediterranean Sea, involving 9 commercial vessels, real-time collecting a list of GNSS meteo parameters: the signals from Galileo, GPS, GLONASS and Beidou constellations, measurements of pressure, temperature, humidity, wind and precipitation. These 9 moving platforms are complemented by a number of fixed ground platforms, used as a reference.</p><p>The difficulties in ship based GNSS meteorology, with respect to the classical approaches from fixed stations, lie both in the exposure of the hardware instruments to challenging environmental conditions as in the open sea and in the computation algorithms, which must be applied to kinematic conditions and continuously solve the receiver position with very high accuracy.</p><p>Two different processing schemes have been applied to the dataset (i.e. few months): the first one is based on differential GNSS using the TRACK suite of GAMIT software, and the second one is based on precise point positioning using the GLAB software. As it is well known, if network solutions are adopted (as in the first case), the satellites and receivers clock errors can be eliminated with very high accuracy, while PPP-based methods (as in the second case) require ultrafast precise satellite ephemeris products, but they give the possibility to implement standalone instruments, so not to send large amounts of full RINEX files to a ground processing centre.</p><p>The ZPD quantities retrieved from the first period of observations aboard ships are shown, using both the techniques. The comparison shows some discrepancies both in the absolute quantity and in the short-term trends. Even if preliminary, the comprehension of the quality of such an unprecedent source of information is of great interest, because the perspectives of this infrastructure are both scientific and operational, thinking for example to the data assimilation into numerical weather prediction models.</p>


2020 ◽  
Vol 10 (7) ◽  
pp. 2478 ◽  
Author(s):  
Woosik Lee ◽  
Eun Suk Suh ◽  
Woo Young Kwak ◽  
Hoon Han

Mobile communication technology is evolving from 4G to 5G. Compared to previous generations, 5G has the capability to implement latency-critical services, such as autonomous driving, real-time AI on handheld devices and remote drone control. Multi-access Edge Computing is one of the key technologies of 5G in guaranteeing ultra-low latency aimed to support latency critical services by distributing centralized computing resources to networks edges closer to users. However, due to its high granularity of computing resources, Multi-access Edge Computing has an architectural vulnerability in that it can lead to the overloading of regional computing resources, a phenomenon called regional traffic explosion. This paper proposes an improved communication architecture called Hybrid Cloud Computing, which combines the advantages of both Centralized Cloud Computing and Multi-access Edge Computing. The performance of the proposed network architecture is evaluated by utilizing a discrete-event simulation model. Finally, the results, advantages, and disadvantages of various network architectures are discussed.


Author(s):  
Reshma P ◽  
Muneer VK ◽  
Muhammed Ilyas P

Face recognition is a challenging task for the researches. It is very useful for personal verification and recognition and also it is very difficult to implement due to all different situation that a human face can be found. This system makes use of the face recognition approach for the computerized attendance marking of students or employees in the room environment without lectures intervention or the employee. This system is very efficient and requires very less maintenance compared to the traditional methods. Among existing methods PCA is the most efficient technique. In this project Holistic based approach is adapted. The system is implemented using MATLAB and provides high accuracy.


1996 ◽  
Vol 176 ◽  
pp. 53-60 ◽  
Author(s):  
J.-F. Donati

In this paper, I will review the capabilities of magnetic imaging (also called Zeeman-Doppler imaging) to reconstruct spot distributions of surface fields from sets of rotationnally modulated Zeeman signatures in circularly polarised spectral lines. I will then outline a new method to measure small amplitude magnetic signals (typically 0.1% for cool active stars) with very high accuracy. Finally, I will present and comment new magnetic images reconstructed from data collected in 1993 December at the Anglo-Australian Telescope (AAT).


2021 ◽  
Vol 11 (11) ◽  
pp. 4758
Author(s):  
Ana Malta ◽  
Mateus Mendes ◽  
Torres Farinha

Maintenance professionals and other technical staff regularly need to learn to identify new parts in car engines and other equipment. The present work proposes a model of a task assistant based on a deep learning neural network. A YOLOv5 network is used for recognizing some of the constituent parts of an automobile. A dataset of car engine images was created and eight car parts were marked in the images. Then, the neural network was trained to detect each part. The results show that YOLOv5s is able to successfully detect the parts in real time video streams, with high accuracy, thus being useful as an aid to train professionals learning to deal with new equipment using augmented reality. The architecture of an object recognition system using augmented reality glasses is also designed.


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