A Real-Time Traffic Detection Method Based on Improved Kalman Filter

Because of its complexity, the Gaussian mixture model algorithm costs many computing resources (CPU cycles) and it is difficult to realise real-time moving object detection on the embedded system. A real-time motion detection method based on an improved Gaussian mixture model is presented in this paper, which is optimised and adjusted for the Gaussian mixture model. It is also optimised on C language and CPU for running on an embedded system. The design of software and hardware on DM6446 is presented in detail including the implementation of the algorithm. The experimental results show that the system can effectively overcome the interference from external environment and realise multi-object real-time motion detection and tracking.

A new image-based real-time flame detection method is proposed in this paper. First, fire flame features based on the HSI color model are extracted by analyzing 70 flame images. Then, based on these flame features, regions with fire-like colors are roughly separated from an image. Besides segmenting fire flame regions, background objects with similar fire colors or caused by color shift resulted from the reflection of fire flames are also separated from the image. In order to get rid of these spurious fire-like regions, the image difference method and the invented color masking technique are applied. Finally, a simple method is devised to estimate the burning degree of fire flames so that users could be informed with a proper warning alarm. The proposed method is tested with seven diverse fire flame video clips on a Pentium II 350 processor with 128 MB RAM at the process speed of thirty frames per second. The experimental results are quite encouraging. The proposed method can achieve more than 96.97% detection rate on average. In addition, the system can correctly recognize fire flames within one second on the initial combustion from the test video clips, which seems very promising.

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.

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.

In this paper, we consider water surface object detection in natural scenes. Generally, background subtraction and image segmentation are the classical object detection methods. The former is highly susceptible to variable scenes, so its accuracy will be greatly reduced when detecting water surface objects due to the changing of the sunlight and waves. The latter is more sensitive to the selection of object features, which will lead to poor generalization as a result, so it cannot be applied widely. Consequently, methods based on deep learning have recently been proposed. The River Chief System has been implemented in China recently, and one of the important requirements is to detect and deal with the water surface floats in a timely fashion. In response to this case, we propose a real-time water surface object detection method in this paper which is based on the Faster R-CNN. The proposed network model includes two modules and integrates low-level features with high-level features to improve detection accuracy. Moreover, we propose to set the different scales and aspect ratios of anchors by analyzing the distribution of object scales in our dataset, so our method has good robustness and high detection accuracy for multi-scale objects in complex natural scenes. We utilized the proposed method to detect the floats on the water surface via a three-day video surveillance stream of the North Canal in Beijing, and validated its performance. The experiments show that the mean average precision (MAP) of the proposed method was 83.7%, and the detection speed was 13 frames per second. Therefore, our method can be applied in complex natural scenes and mostly meets the requirements of accuracy and speed of water surface object detection online.

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.

Gravity and gravity gradient data are widely used in geodesy, geodynamics, oil and mineral exploration, and aided navigation. The measured gravity and gravity gradient data include high-frequency noise caused by instrument system error, environmental conditions, and human factors. Separating noise from the measured gravity and gravity gradient data is one of the most challenging tasks in processing the measured data. Traditional low-pass digital filters can remove the noise of an individual component in real-time, which cannot realize the joint filtering of gravity and gravity gradient data. As a postprocessing method, the inversion-based methods can combine gravity and all the gradient components to remove the noise constrained by the Laplace equation. However, a real-time filter method that combines gravity and all gradient components is needed for some special applications, such as submarine gravity and gravity gradient-aided navigation. In this study, gravity and gravity gradient data are combined in establishing system equation and measurement equation of the standard Kalman filter, and denoised in real-time by the improved Kalman filter (IKF). Based on the model test, this method can simultaneously remove the noise in gravity and gravity gradient data in real-time, and ensure denoising performance. Finally, we apply this method to real gravity and gravity gradient data in St. George’s Bay, Canada, acquired by Bell Geospace, and compared the denoised results by full tensor noise reduction (FTNR) and Gaussian low-pass filter, which verified that the performance of IKF is well in real-time joint filtering of gravity and gravity gradient data.

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.

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.

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 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.

Developing real-time multi-target detection method of soybean quality is a challenging work. This study proposed an innovation strategy that integrating hyperspectral imaging (HSI) and transformer-convolutional autoencoder (T-CAE) with deep learning models to simultaneously identify different damaged soybeans, including broken, infested, diseased, and moldy types. The 1D convolutional neural network (1D-CNN) models based on the T-CAE augmented spectrum can discriminate these different damaged soybeans, with the classification accuracy (Acc) over 96.00 %. Introducing the self-attention mechanism of T-CAE architecture can be a considerable spectral data augmentation strategy to overcome the sample limitation for classification model development. Notably, T-CAE-generated spectra exhibited strong capabilities in capturing global spectral features while preserving local details via convolutional encoding. When used to train 1D-CNN models, they improved Acc by 9.71 % compared to models trained on raw spectrum. Consequently, HSI technique coupled with T-CAE can be a rapid, real-time and multi-target classification strategy for damaged soybeans. • A novel T-CAE strategy was proposed for spectral data augmentation under limited samples. • The strategy integrates hyperspectral imaging with transformer-enhanced convolutional autoencoder. • T-CAE-augmented spectra significantly improved 1D-CNN classification accuracy (>96 %). • The approach enables real-time, multi-target detection of broken, infested, diseased, and moldy soybeans.

An improved tiny-yolov3 pedestrian detection algorithm

This paper proposes a real-time detection method for a car driving ahead in real time on a tunnel road. Unlike the general road environment, the tunnel environment is irregular and has significantly lower illumination, including tunnel lighting and light reflected from driving vehicles. The environmental restrictions are large owing to pollution by vehicle exhaust gas. In the proposed method, a real-time detection method is used for vehicles in tunnel images learned in advance using deep learning techniques. To detect the vehicle region in the tunnel environment, brightness smoothing and noise removal processes are carried out. The vehicle region is learned after generating a learning image using the ground-truth method. The YOLO v2 model, with an optimal performance compared to the performances of deep learning algorithms, is applied. The training parameters are refined through experiments. The vehicle detection rate is approximately 87%, while the detection accuracy is approximately 94% for the proposed method applied to various tunnel road environments.