Flame detection algorithm based on a saliency detection technique and the uniform local binary pattern in the YCbCr color space

Liu, Zhao-Guang; Yang, Yang; Ji, Xuanrong

doi:10.1007/s11760-014-0738-0

Cited by 56 publications

(16 citation statements)

References 23 publications

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“…Since the computing power of a single edge computing device is relatively weak, how to combine multiple edge computing devices to process services in a distributed manner, how to solve the best job sharing rate based on the computer and communication functions of edge computing devices, and how to use the proportion of subtasks and offload the distributed parallel computing on each MEC device have become urgent issues in the SD-CEN architecture. In order to solve the above problems, this document first models the Mobile Information Systems service response delay in the SD-CEN architecture based on integrated communication and computer technology [23].…”

Section: Mobile Terminal Module a Mobile Terminal (Mt)mentioning

confidence: 99%

Digital Media Application Technology of Mobile Terminals Based on Edge Computing and Virtual Reality

Jiang

2021

Mobile Information Systems

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With the maturity of the most advanced technology and virtual reality, the application of digital media technology in various fields has become more and more extensive. Digital media technology has greatly promoted the development of all classes of society. Therefore, how to develop digital media technology applications has become a major issue. This article aims to study the digital media application technology of mobile terminals based on edge computing and virtual reality. In this paper, we use edge computing and virtual reality methods to study the digital media application technology of mobile terminals. We used the SD-CEN architecture and FWA in edge computing to conduct simulation experiments and compared the delay time performance of the FWA with that of the PSO-CO algorithm, WRR algorithm, and Pick-KX algorithm. The results of this paper show that it can effectively reduce the response delay of real-time face recognition services and improve user experience. Compared with the traditional cloud computing architecture and a single MEC device, the SD-CEN network architecture based on FWA strategy has more advantages. The latency of the FWA is increased by 61%, 46%, and 17%, respectively, compared with that of the WRR, Pick-KX, and PSO-CO algorithms.

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Section: Mobile Terminal Module a Mobile Terminal (Mt)mentioning

confidence: 99%

Digital Media Application Technology of Mobile Terminals Based on Edge Computing and Virtual Reality

Jiang

2021

Mobile Information Systems

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“…However, this work is evaluated using a database with videos and images, not in a real scenario. The researchers in [18] proposed a new flame detection algorithm based on a saliency detection technique and the Uniform Local Binary Pattern (ULBP). Moreover, they used an exponential function with two parameters to model the flame area's texture to reduce the number of false alarms.…”

Section: Image Processing For Flame/fire Focus Detectionmentioning

confidence: 99%

HelpResponder—System for the Security of First Responder Interventions

Rodriguez‐Sanchez

Fernández-Jiménez

Jiménez

et al. 2021

Sensors

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Firefighter’s interventions under dense smoke and flames are hazardous and ideally need an efficient in-advance geo-located actuation plan. The existing communication and sensing technologies should be customized, optimized, and integrated to better know the conditions (flame locations, air condition) before and during the rescue team’s interventions. In this paper, we propose a firefighter intervention architecture, which consists of several sensing devices (flame detectors, carbon dioxide air content) a navigation platform (an autonomous ground wheeled robot), and a communication/localization network (BLE IoT network) that can be used before and during an intervention in rescue or fire extinguishing missions even for indoor or confined spaces. The paper’s key novelty presents our integrated solution, giving some key implementation details and an intensive experimentation campaign in two real firefighter scenarios with real controlled fires. Results carried out in these real indoor scenarios are presented to demonstrate the feasibility of the system. A fire detection system is proposed to improve fire focus in real time and moving in confined spaces with no visibility and physical references. The results obtained in the experimentation show the proposal’s effectiveness in locating the fire focus’s position and orientation reducing time and risk exposure. This kind of location-aware fire integrated systems would significantly impact the speed and security of first responder interventions.

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“…To reduce the feature dimension, the LBP using the circular neighborhood system is further extended to "uniform pattern" LBP [31]. It means that the number of hops of adjacent binary values in the binary mode of LBP is not greater than 2.…”

Section: International Journal Of Aerospace Engineeringmentioning

confidence: 99%

A Fast Recognition Method for Space Targets in ISAR Images Based on Local and Global Structural Fusion Features with Lower Dimensions

Yang

Zhang

Ding

2020

International Journal of Aerospace Engineering

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Feature extraction is the key step of Inverse Synthetic Aperture Radar (ISAR) image recognition. However, limited by the cost and conditions of ISAR image acquisition, it is relatively difficult to obtain large-scale sample data, which makes it difficult to obtain target deep features with good discriminability by using the currently popular deep learning method. In this paper, a new method for low-dimensional, strongly robust, and fast space target ISAR image recognition based on local and global structural feature fusion is proposed. This method performs the trace transformation along the longest axis of the ISAR image to generate the global trace feature of the space target ISAR image. By introducing the local structural feature, Local Binary Pattern (LBP), the complementary fusion of the global and local features is achieved, which makes up for the missing structural information of the trace feature and ensures the integrity of the ISAR image feature information. The representation of trace and LBP features in a low-dimensional mapping feature space is found by using the manifold learning method. Under the condition of maintaining the local neighborhood relationship in the original feature space, the effective fusion of trace and LBP features is achieved. So, in the practical application process, the target recognition accuracy is no longer affected by trace function, LBP feature block number selection, and other factors, realizing the high robustness of the algorithm. To verify the effectiveness of the proposed algorithm, an ISAR image database containing 1325 samples of 5 types of space targets is used for experiments. The results show that the classification accuracy of the 5 types of space targets can reach more than 99%, and the recognition accuracy is no longer affected by the trace feature and LBP feature selection, which has strong robustness. The proposed method provides a fast and effective high-precision model for space target feature extraction, which can give some references for solving the problem of space object efficient identification under the condition of small sample data.

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Flame detection algorithm based on a saliency detection technique and the uniform local binary pattern in the YCbCr color space

Cited by 56 publications

References 23 publications

Digital Media Application Technology of Mobile Terminals Based on Edge Computing and Virtual Reality

Digital Media Application Technology of Mobile Terminals Based on Edge Computing and Virtual Reality

HelpResponder—System for the Security of First Responder Interventions

A Fast Recognition Method for Space Targets in ISAR Images Based on Local and Global Structural Fusion Features with Lower Dimensions

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