Instance segmentation has drawn mounting attention due to its significant ***,high computational costs have been widely acknowledged in this domain,as the instance mask is generally achieved by pixel-level *** this pa...
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Instance segmentation has drawn mounting attention due to its significant ***,high computational costs have been widely acknowledged in this domain,as the instance mask is generally achieved by pixel-level *** this paper,we present a conceptually efficient contour regression network based on the you only look once(YOLO)architecture named YOLO-CORE for instance *** mask of the instance is efficiently acquired by explicit and direct contour regression using our designed multiorder constraint consisting of a polar distance loss and a sector *** proposed YOLO-CORE yields impressive segmentation performance in terms of both accuracy and *** achieves 57.9%AP@0.5 with 47 FPS(frames per second)on the semantic boundaries dataset(SBD)and 51.1%AP@0.5 with 46 FPS on the COCO *** superior performance achieved by our method with explicit contour regression suggests a new technique line in the YOLO-based image understanding ***,our instance segmentation design can be flexibly integrated into existing deep detectors with negligible computation cost(65.86 BFLOPs(billion float operations per second)to 66.15 BFLOPs with the YOLOv3 detector).
In the environment of smart examination rooms, it is important to quickly and accurately detect abnormal behavior(human standing) for the construction of a smart campus. Based on deep learning, we propose an intellige...
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In the environment of smart examination rooms, it is important to quickly and accurately detect abnormal behavior(human standing) for the construction of a smart campus. Based on deep learning, we propose an intelligentstanding human detection (ISHD) method based on an improved single shot multibox detector to detect thetarget of standing human posture in the scene frame of exam room video surveillance at a specific examinationstage. ISHD combines the MobileNet network in a single shot multibox detector network, improves the posturefeature extractor of a standing person, merges prior knowledge, and introduces transfer learning in the trainingstrategy, which greatly reduces the computation amount, improves the detection accuracy, and reduces the trainingdifficulty. The experiment proves that the model proposed in this paper has a better detection ability for the smalland medium-sized standing human body posture in video test scenes on the EMV-2 dataset.
Spike camera is a retina-inspired neuromorphic camera which can capture dynamic scenes of high-speed motion by firing a continuous stream of spikes at an extremely high temporal resolution. The limitation in the curre...
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Vehicle-to-Everything(V2X) communications will be an essential part of the technology in future autonomous drive decision systems.A fundamental procedure is to establish a robust communication channel between end-to-e...
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Vehicle-to-Everything(V2X) communications will be an essential part of the technology in future autonomous drive decision systems.A fundamental procedure is to establish a robust communication channel between end-to-end *** to the antenna placed at different positions on vehicles,the existing cellular electro-magnetic(EM) wave propagation modelling does not fit properly for V2X direct communication *** order to figure out a feasible understanding of this problem,this paper focuses on the propagation channel analysis in a rural Vehicle-to-Vehicle(V2V) scenario for vehicular communication with antenna position experiments at different *** adopting the ray-tracing algorithm,a rural scenario simulation model is built up via the use of a commercial-off-the-shelf(COTS) EM modelling software package,that computes the path loss received power and delay spread for a given propagation ***,a real-world vehicle measurement campaign was performed to verify the simulation *** simulated and measured receiver power was in good agreement with each other,and the results of this study considered two antenna types located at three different relative heights between the two *** research provides constructive guidance for the V2V antenna characteristics,antenna placement and vehicle communication channel analysis.
A mobile ad hoc network (MANET) is an independent wireless temporary network established by employing a set of mobile nodes (i.e. laptops, smartphones, iPods, etc.) appropriate for the environment in which the network...
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A mobile ad hoc network (MANET) is an independent wireless temporary network established by employing a set of mobile nodes (i.e. laptops, smartphones, iPods, etc.) appropriate for the environment in which the network infrastructures are not fixed. The most common problems faced by MANET are energy efficiency, high energy consumption, low network lifetime as well as high traffic overhead which create an impact on overall network topology. Hence, it is necessary to provide an energy-effective CH election to take steps against such issues. Therefore, this paper proposes a novel model to enhance the network lifetime and energy efficiency by performing a routing strategy in MANET. In this paper, an optimal CH is selected by proposing a novel Fuzzy Marine White Shark optimization (FMWSO) algorithm which is obtained by integrating fuzzy operation with two optimization algorithms namely the marine predator algorithm and white shark optimizer. The proposed approach comprises three diverse stages namely Generation of data, Cluster Generation and CH selection. A novel FMWSO algorithm is proposed in such a way to determine the CH selection in MANET thereby enhancing the network topology, network lifetime and minimizing the overhead rate, and energy consumption. Finally, the performance of the proposed FMWSO approach is compared with various other existing techniques to determine the effectiveness of the system. The proposed FMWSO approach consumes minimum energy of 0.62 mJ which is lower than other approaches.
Wireless Sensor Networks (WSNs) face critical energy efficiency challenges due to resource limitations, especially in extending network lifetime. This paper presents a reinforcement learning-based solution combining L...
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Complementary-label learning(CLL)aims at finding a classifier via samples with complementary *** data is considered to contain less information than ordinary-label *** transition matrix between the true label and the ...
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Complementary-label learning(CLL)aims at finding a classifier via samples with complementary *** data is considered to contain less information than ordinary-label *** transition matrix between the true label and the complementary label,and some loss functions have been developed to handle this *** this paper,we show that CLL can be transformed into ordinary classification under some mild conditions,which indicates that the complementary labels can supply enough information in most *** an example,an extensive misclassification error analysis was performed for the Kernel Ridge Regression(KRR)method applied to multiple complementary-label learning(MCLL),which demonstrates its superior performance compared to existing approaches.
Lung cancer is among the primary reasons of cancer-related fatalities. Globally, that underscoring urgent need for efficient early detection techniques. The aim of this study to estimate and contrast the capabilities ...
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In this paper we study the task of a single-view image-guided point cloud completion. Existing methods have got promising results by fusing the information of image into point cloud explicitly or implicitly. However, ...
The COVID-19 pandemic has resulted in a significant increase in the number of pneumonia cases, including those caused by the Coronavirus. To detect COVID pneumonia, RT-PCR is used as the primary detection tool for COV...
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