A new image-based real-time flame detection method using color analysis

Dynamic target recognition is an important issue in the field of image processing research. It is widely used in photoelectric detection, target tracking, video surveillance areas. Complex cruise scene of target detection, compared to the static background, since the target and background objects together and both are in motion, greatly increases the complexity of moving target detection and recognition. Based on the practical engineering applications, combining an embedded systems and real-time image detection technology, this paper proposes a real-time movement detection method on an embedded system based on the FPGA + DSP system architecture on an embedded system. The DSP digital image processing system takes high speed digital signal processor DSP TMS320C6416T as the main computing components. And we take large capacity FPGA as coprocessor. It is designed and developed a high-performance image processing card. The FPGA is responsible for the data receiving and dispatching, DSP is responsible for data processing. The FPGA collects image data and controls SDRAM according to the digital image sequence. The SDRAM realizes multiport image buffer. DSP reads real-time image through SDRAM and performs scene motion detection algorithm. Then we implement the data reception and data processing parallelization. This system designs and realizes complex cruise scene motion detection for engineering application. The image edge information has the anti-light change and the strong anti-interference ability. First of all, the adjacent frame and current frame image are processed by convolution operation, extract the edge images. Then we compute correlation strength and the value of movement offset. We can complete scene motion parameters estimation by the result, in order to achieve real-time accurate motion detection. We use images in resolution of 768 576 and 25Hz frame rate to do the real-time cruise experiment. The results show that the proposed system achieves real-time processing requirements for the engineering applications. 2014 SPIE.

Digital Image Processing based Fire Flame Color and Oscillation Frequency Analysis

Fast fire flame detection in surveillance video using logistic regression and temporal smoothing

Inadequate crawling behavior of the crawler will have a very serious impact on the site, so anti crawling mechanism is an important function for the website. Most of the existing anti crawling methods are non real time detection, and the recognition accuracy is low. By analyzing the characteristics of Crawler, a real-time crawler detection method based on sliding time window is proposed, which improves the accuracy and efficiency of detection of non compliance with the rules of Crawler.

Environment perception is essential for autonomous driving technology. The curb is a prominent feature of urban roads and therefore is a significant part of environment perception. In this paper, a real-time curb detection and tracking method is proposed for Unmanned Ground Vehicles (UGVs). The proposed curb detection algorithm uses the the surrounding environment data provided by a 3D-LIDAR sensor to extract the curb position based on its spatial features. The curb tracking algorithm is proposed to predict and update the curb position with respect to the current vehicle states in real time. The performance of the proposed method is verified through extensive experiments with a UGV driving on campus roads. The experimental results demonstrate the accuracy and robustness of the proposed method.

The combination of UAV camera and intelligent algorithm is a promising method for non-contact bridge crack detection. In this paper, an inspection tool based on UAV Image Acquisition Technology (UAVIAT) and Improved Intelligent Target Detection Technology (IITDT) called Improved Intelligent Real-Time Crack Detection Method for Bridges (IIRTCDMB) is proposed for efficient crack detection. The contributions of this paper are (1) The Squeeze-Excitement (SE) attention module is integrated into the target detection algorithm - You Only Look Once version 7 (YOLOv7) model to improve the learning ability of the feature channel. A Focal-efficient intersection over union (Focal-EIoU) loss function is also introduced to improve the regression accuracy of the model. As a result, a new crack image detection algorithm, YOLOv7-CD, is proposed. (2) A training process based on two-stage transfer learning (TSTL) is established, and hyper-parameter optimization of YOLOv7-CD is carried out. The feasibility and excellent performance of the proposed method are verified by applying it on the Cuntan Yangtze River Bridge. The results show that the average precision (AP) of the YOLOv7-CD model is improved by 3.19% compared with the original YOLOv7 model. After TSTL and hyperparameter optimization, the AP of the YOLOv7-CD model for bridge crack detection reaches 98.01%, which is higher than that of the popular target detection models. The IIRTCDMB proposed in this paper can acquire bridge surface images more safely and efficiently, and provide inspectors with more accurate structural crack information with lower computational and hardware requirements, which can provide technical support for the assessment of structural safety conditions and the formulation of maintenance programs.

Currently, the existing fault detection methods have problems in that the detection results are not in line with reality and the detection accuracy is too low when detecting the grounding circulation fault of transmission cables. This paper introduces the wavelet energy spectrum of module components to design and study the real-time fault detection method of grounding the circulating current of the transmission cable. In this study, the wavelet-based detection function that meets the requirements of fault detection is selected. On this basis, the traveling wave fault features are extracted by using the wavelet energy spectrum of the module component. Finally, the fault detection signal is decomposed in real-time, and the fault detection is realized according to the practical value of the reconstructed signal. The comparative experiment proves that the new detection method can get the detection result of the grounding current fault of the cable, which is more in line with the actual situation and has very high detection accuracy.

Walnut is one of the important economic tree species in the world. For the past few years, with the continuous expansion of walnut planting area, walnut bacterial blight caused by Xanthomonas campestrispv. juglandis has critically affected walnut production and caused economic losses. In this research, by comparing the gene sequences of X. campestrispv. juglandis and its closely related bacterial species, the 16S rDNA gene sequence was selected, and the specific primers were evaluated using PCR. Subsequently, the real-time fluorescence PCR specific detection method of X. campestrispv. juglandis was established and optimized. The specificity of real-time PCR was verified for 23 strains isolated from walnut and its surrounding soil, including eight target pathogens. The specific amplification was possible even when the cDNA template concentration was 7.0×10-5 µg/mL, hence, the detection sensitivity of the standard method is 7.0×10-5 µg/mL. The reliability of the established real-time PCR detection method in field detection was verified by the identification of target bacteria in 8 randomly collected healthy leaves and leaves of suspected walnut blight. This method laid a foundation for the early diagnosis of X. campestrispv. juglandis.

For the lack of current extraction methods of traffic basic data, a traffic information acquisition method based on improved Kalman filtering using traffic video was presented in this paper. The Gaussian mixture model was improved for multi-vehicle moving targets detection. In order to further improve the detection efficiency, a heuristic improvement method was proposed. For the matching problem of multiple targets in the continuous video frame, combining the vehicle movement characteristic, the Kalman filter was used to estimate the vehicle position optimally, a real-time traffic detection method of matching the target chain was proposed. Finally, the experiment was carried out with the actual transportation video, results show that the proposed method can effectively improve the noise interference and foreground blurring in Multi-target vehicle detection, and can extract the vehicle moving target information from different traffic environments with high accuracy, different models and vehicle color The lowest detection rate was 93.08%.

For the false data injection attack (FDIA) in the consensus-based distributed estimation, the information of weight matrix is critical for both attackers and defenders. In this paper, we study the impact of the weight matrix on the FDIA and the attack mitigation techniques. We first propose a learning-based cooperative FDIA strategy, where malicious nodes acquire the information of weight matrix cooperatively and launch the attack covertly. Two types of FDIA, the sudden FDIA and the dynamic FDIA, are considered to tamper the consensus result of the network to a pre-designed false value. Moreover, using the obtained information of weight matrix, a real-time surveillance and response mechanism is constructed to enhance the covertness of FDIA strategy. Since the surveillance and response mechanism can bypass existing FDIA detection methods, we further investigate the attack mitigation techniques against the learning-based cooperative FDIA. A real-time FDIA detection method and a reassessment mechanism with a punishment scheme are presented to resist the surveillance and response mechanism in the learning-based cooperative FDIA. Comprehensive simulation results verify that the attacker can obtain the information of weight matrix, and tamper the consensus result to a false value by launching the learning-based cooperative FDIA. And the real-time FDIA detection method can detect the malicious nodes efficiently and promptly.

Deep Learning is the latest research achievement in the field of Artificial Intelligence. To detect and identify the state of circuit breaker in ambient air automatic monitor station, Deep Learning was proposed to realize the circuit breaker state recognition in real-time. The Deep Convolutional Neural Network was used to construct the state detection and identification model of the circuit breaker and provided the detection process. The remote monitoring system of ambient air automatic monitor station and inspection data were used to build the experimental data platform, with the circuit breaker state recognition data was acquired from the multiple ambient air automatic monitor stations. Through training the model with the Deep Learning, the experimental results showed that the circuit breaker state detection and identification method have a simple recognition process and high accuracy. The real-time Detection and Identification function of the circuit breaker state was realized, meanwhile, the assistant decision-making function was provided to the ambient air automatic monitor station.

As one of the most common structural forms in port engineering, the operation environment of high-pile wharf is quite harsh and complex, and its pile foundation often produces structural damage of different degrees. Until now, there is a lack of efficient, safe and economic damage detection methods. A novel and precise real-time structural damage detection (SDD) method using both finite element modelling (FEM) and 1D convolutional neural networks (CNNs) is established in this study. The results indicate that the proposed method could accurately identify the presence and location of damage in real time. The results also demonstrated that the proposed 1D CNNs based model are more sensitive to the longitudinal and lateral displacement responses of the high-pile wharf structure.

Vehicle detection is an important component in unmanned driving systems. This paper presents a real-time vehicle detection method under complex road conditions to solve the problem of real-time detection of road vehicles in automatic driving. Firstly using yolo network to build a deeper depth neural network, which is used to identify and score the vehicles in the image. Then, using the dynamic threshold method to remove some false candidate boxes, and using the Gauss attenuation function to filter the overlapping candidate boxes. The loss function normalized by the change rate is used to train the neural network, and the batch normalization layer is used to correct the input data of the network to avoid data deviation during the training process. Finally, a full convolution layer is added at the end of the network layer to transform the two-dimensional data into one-dimensional data and output the final recognition results. It is verified that this method improves the efficiency and accuracy of real-time vehicle detection, it can effectively detect vehicles on roads with complex backgrounds, and satisfy the real-time requirements of road vehicle detection.

This paper presents the results of a pilot-in-the-loop experiment performed to investigate the efficacy of a pilot induced oscillation (PIO) or adverse rotorcraft-pilot coupling (RPC) real-time detection method, to be implemented as an in-cockpit warning system. A test pilot performed a number of simulated flights inside the Heliflight-R simulator at the University of Liverpool. Two handling qualities (HQ) mission task element (MTE) maneuvers were chosen, namely Precision Hover and Lateral Reposition. The baseline dynamics were those of a FLIGHTLAB BO105-like helicopter model, as used in previous tests; changes in rate limits were introduced to induce the pilot-vehicle system (PVS) to be more RPC/PIO prone, and to observe pilot’s adaptation to these variations causing system instabilities during the chosen MTEs. To objectively measure the severity of the PIO encountered during the tests, the PhaseAggression Criterion (PAC) has been used. This method has been developed to allow for real-time PIO detection in order to provide the information inside the cockpit. In addition, pilot subjective ratings were collected, by using the HQs, PIO and Pilot Workload rating scales. Overall, the results show a good correlation between objective and subjective evaluations, and that it is possible to detect PIOs in real-time. The information can be provided to the pilot by means of visual, aural or haptic cues, which is the work the authors are currently carrying out.

Real-time situational awareness tools are of critical importance to power system operators, especially during emergencies. The availability of electric power has become a linchpin of most post disaster response efforts as it is the primary dependency for public and private sector services, as well as individuals. Knowledge of the scope and extent of facilities impacted, as well as the duration of their dependence on backup power, enables emergency response officials to plan for contingencies and provide better overall response. Based on real-time data acquired by Frequency Disturbance Recorders (FDRs) deployed in the North American power grid, a real-time detection method is proposed. This method monitors critical electrical loads and detects the transition of these loads from an on-grid state, where the loads are fed by the power grid to an off-grid state, where the loads are fed by an Uninterrupted Power Supply (UPS) or a local backup generation system. The details of the proposed detection algorithm are presented, and some case studies and off-grid detection scenarios are also provided to verify the effectiveness and robustness. Meanwhile, how to implement the detection tool in real-time environment and its achievements in detecting off-grid situations are discussed in this paper.