Photoelectric Detection Technology Utilizing Communication Remote Sensing Image Data

In the photoelectric detection system, the photoelectric detector can convert the optical signal to be measured into a current signal, and the current amplifier transforms the current signal output by the detector into a voltage signal for amplification. In this study, the photo-multiplier tube (PMT) is selected as the photoelectric detector. Compared with other photoelectric detectors, it can obtain higher internal gain, higher sensitivity, and better response performance. The current amplifier is prepared by pre-amplifier and voltage amplifier. In order to capture photoelectric signals well, a large-format scanning system is set up to design each component module, control module, and host computer module of the system. Besides, a machine learning-based algorithm is proposed, namely semi-supervised manifold image recognition algorithm, which is used for identify photoelectric detection images. In the test process, the printed circuit board (PCB) and sapphire material are firstly used as the substrate of the current amplifier, and their influences in the circuit are compared. The peak value of the output noise of each substrate circuit is around 2.8 mV when the input of the current amplifier is short-circuited. Then, the signal gain and signal bandwidth of the photoelectric detection system remain stable when there is no optical signal input. During the process of changing the system signal gain ratio, the noise output of the system is the lowest when the voltage of PMT is 0.50 V and the current amplifier gain is set to 2.2 × 105 V/A. The proposed recognition algorithm can identify different types of targets well. After the image is projected into a two-dimensional space by the algorithm, the distance between classes increases, and the targets in the class promote aggregation, thereby enhancing the identify-ability between samples.

Self-powered silicon PIN photoelectric detection system based on triboelectric nanogenerator

To solve the unstable problem of target tracking detection system, this paper proposes an improved mean-shift algorithm for object tracking, establishes object tracking processing model;provides the processing algorithm of object tracking. According to the principal of object tracking, papersets up sky background brightness calculation model in photoelectric tracking optical detection area and detection capability calculation model of space object, analyzes the effect of background illumination on the signal to noise ratio(SNR) of photoelectric tracking system, gives the change curve of detection capability based on the exposure time of CCD camera, SNR threshold of photoelectric detection system and dark current of photoelectric detection system. Through calculation and test , paperprovidesthe comparison results of the improved mean-shift and traditional mean-shift, verifies the correctness of the proposed algorithm and calculation models for photoelectric detection capability in target tracking detection system, the results show the improved mean-shiftobject tracking algorithm and detection capability calculation model are correct.

The traditional photoelectric detection systems are mostly designed based on a single task and cannot meet the needs of target detection and identification for different tasks. A concept of adaptive photoelectric detection system based on the system parameter library has been proposed. The method of analyzing the measurement data in orbit and correcting the system parameters adaptively for different task requirements is described. This paper focuses on the establishment of the system parameter library and the method of selecting the working band adaptively and identifying the scenes. For point target detection, the synthesize signal-to-noise ratio are used as the basis for parameter adjustment. The adaptive photoelectric detection system generates the feedback of parameter adjustment and adjusts parameters including integration time, frame rate, pixel size and quantization bits to achieve the best detecting mode.

Rail trains are an indispensable part of the transportation, so technological innovations such as artificial intelligence will inevitably promote the overall development of unmanned driving technology in this field, how to ensure the safety of unmanned trains is related to intelligent rail detection technology is very important, such as the detection of foreign body intrusion on rails, turnouts, curved rails, etc. These technologies have become the basic technology of rail train automatic driving technology. This paper divides the key technology of rail intelligent detection into three parts, and conducts in-depth analysis and research from these three aspects: curved track detection and warning module, turnout detection and warning module, obstacle detection and warning module, and combined with the actual needs of each module, Continuously through experiments, choose more suitable and optimal algorithms to achieve and then integrate the module algorithms to achieve the synchronization of the work between each module without interfering with each other, and design and implement the intelligent rail detection and early warning system to enhance the interactive experience. According to the functional requirements of different modules of the system, this paper selects core algorithms such as curvature radius, Hough transforms and frame difference method to improve each module, and verifies the feasibility of the selected algorithm.KeywordsCurvature radiusHough probability transformationFrame difference method

Development of an intelligent multi-scale defect detection system for aspheric lenses with high accuracy and efficiency

The changed environment factors will affect the capture rate of target and the detection performance in photoelectric detection screen testing system, to improve the detection performance of system, it is very necessary to establish the target optical characteristic model and correlation detection probability calculation model. Based on the detection principle of unit photoelectric detection screen and the spatial geometric relation of four photoelectric detection screens testing system, this article establishes a new target radiation characteristics calculation model by using target's radiation luminance and radiation energy, gives the output signal expression function, derived the detection probability function by signal to noise ratio (SNR) and false alarm probability. Through calculation analysis, the results show that the detection probability of four photoelectric detection screens testing system is related to the target optical characteristic, the output photoelectric current and SNR of photoelectric detection receiving sensor can describe target detection performance. According to the experiments testing, the proposed calculation model can effectively evaluate the performance of photoelectric detection screen testing system.

A white light photoelectric aerosol detection system suitable for free atmospheric determination of particle concentration from ground level to about 28 km altitude is described. Particle size distributions obtained in situ over the radius range of approximately 0.16 to 1 μm on three soundings are presented. The tropospheric distributions show close to power-law dependence, dN/dlogr=r−3.3, whereas the stratospheric distributions have a steeper power law slope for r≳0.3 μm and a flatter slope for r≳0.3 μm, agreeing well with a recent impactor-derived distribution using a new analysis model. These findings question the validity of earlier globally representative distribution models.

The dynamic deflection measurement is an important component to the bridge loading test of high-speed railway bridge. Using inclinometers to test the Dynamic deflection is a good method for High-Speed Railway bridge deflection detection. In this paper, we develop a new inclinometer and a new theory to fast calculate deflection. It has been use to detect the dynamic deflection of Baishui River bridge on FuZhou-WenZhou High-Speed Railway when high speed train runs across at kinds of speed. Results indicate: comparing with the measurement results of photoelectric deflection detection system, all dynamic deflection measurement results of inclinometers are very accuracy. Their deflection curves are also similar. All results also show: Using our inclinometers to test High-Speed Railway bridge dynamic deflection can get precise results and can satisfy the dynamic deflection measurement request of high-speed railway bridge. Comparing with other methods of dynamic deflection measurement, this method is very easy to operate., do not need stop the traffic, and not affected by weather, and not need a static reference point, can measure the dynamic deflection of any beam bridge.

Photoelectric detector surface damage state polarization imaging type detection system

A kind of photoelectric detection method based on black-white bar code and modern photoelectric detection technology is put forward for measuring the displacement deviation of printing press cylinder surface in high-speed motion state. A high-precision noncontact photoelectric detection system is designed and implemented. The accurate testing bar code cooperates with the high-speed A/D mixed signal processing system, the position deviation of every point on the circumference of the printing press system in high-speed measurement state can be obtained, low measurement error which is in the range of ±10μm. The given experiment results indicate that: the system has stable working condition, high measurement speed, and the system provide a new kind of monitoring technology to effectively deal with the overprint deviation.

The ministry of transport officially released the implementation plan for the construction of highway long- span bridge structural health monitoring system on march 1, 2021, which is doomed to promote the popularization and application of bridge structure health monitoring. This paper innovatively combines UAV(Unmanned Aerial Vehicle) and IOT(Internet of Things) technology, and carries out practical engineering application in bridge quality detection project. Firstly, based on the research status of bridge health monitoring, the design principles, reference laws and code of intelligent bridge inspection system are sorted out, and a three-layer intelligent bridge detection system based on IOT is designed. The main components include low-power consumption wireless sensor, edge data acquisition device, gateway and health big data analysis center. This paper focuses on the practical application of UAV and IOT technology in intelligent detection of bridge engineering. The innovations include targeted deployment and monitoring of various types of sensors according to the actual bridge structure and on-site conditions, as well as targeted design of UAV flight routing and important detection sites. Finally, this paper discusses the research status of bridge structural damage identification and health status assessment. Although some progress has been made, there are still many difficulties to be overcome for practical engineering applications. Through the introduction of the above practical engineering application cases, it is hoped to provide some guidance for the research of bridge health monitoring and intelligent detection.

In recent years, with the progress of society and the development of the economy, the number of cars in China has been increasing. In wireless communication networks, the choice of wireless nodes has a greater impact on the improvement of system performance (such as channel capacity, coverage area, etc.). The intelligent vehicle target detection system can perceive and recognize the surrounding objects such as pedestrians and vehicles through sensors, which is the basis for realizing the unmanned driving of intelligent vehicles. In a wireless environment where multiple wireless nodes coexist, current research focuses on how long it takes to re-plan the selection of wireless nodes (for mobile environments) and how to allocate and manage wireless nodes, such as where under the conditions of wireless, who will wireless with whom, and in what way (such as auto focus, digital fusion, or other) wireless, etc.; the dedicated centralized controller (such as the base station of the infrastructure-based wireless access network) determines the wireless partner (i.e., centralized type selection), or the wireless node decides by itself (i.e., distributed selection); select the appropriate number of wireless nodes to take into account the system performance gain and implementation complexity. The popularity of automobiles has brought great convenience to people's lives, but it has also brought greater traffic pressure. At present, the amount of domestic automobile traffic has increased exponentially, and urban traffic congestion has become more serious, and even caused serious traffic accidents, directly affecting people's quality of life. The emergence of intelligent transportation systems has effectively alleviated road traffic pressure and reduced the incidence of traffic accidents. As an important part of the intelligent transportation system, the research of intelligent vehicles has received extensive attention. Intelligent vehicles use a variety of sensors installed on the body to sense the environment, and realize intelligent driving functions such as lane line detection, obstacle detection, dynamic cruise control, and unmanned driving, which is conducive to reducing the incidence of traffic accidents and improving the safety of vehicle driving. At present, in the research of target recognition and tracking of intelligent vehicles, the traditional target detection method is mainly based on artificial feature extraction, which is difficult to describe more complex or higher-order image features, and the tracking effect is not good, which limits the target detection. An intelligent vehicle is an intelligent system with functions such as environment perception, planning decision-making, and operation control, and is an important part of the intelligent transportation system. Identify the effect. In response to these problems, we present a study of the target recognition and tracking of intelligent vehicles based on grid map and lidar sensor technology. To verify the proposed method in the actual scene, we identify and track the vehicle ahead on the road through a car equipped with a lidar sensor. The meaning of target detection: target detection, also called target extraction, is an image segmentation based on target geometric and statistical features. The results show that the proposed research method can accurately identify the vehicle in front and detect the moving targets such as pedestrians. The tracking trajectory is also consistent with the expected route. The results show that the laser radar-based vehicle target recognition and tracking in this study is effective. In the 10 frames of images, the average time taken for the recognition of one frame of image is 9.8 ms, while the traditional method is 12.9 ms. Technology can improve reliable road information for intelligent vehicle driving and improve the transportation efficiency and safety performance of intelligent vehicles. There are two main target detection methods for lidar: detection methods based on feature extraction and detection methods based on grid maps.

A population explosion has resulted in garbage generation on a large scale. The process of proper and automatic garbage collection is a challenging and tedious task for developing countries. This paper proposes a deep learning-based intelligent garbage detection system using an Unmanned Aerial Vehicle (UAV). The main aim of this paper is to provide a low-cost, accurate and easy-to-use solution for handling the garbage effectively. It also helps municipal corporations to detect the garbage areas in remote locations automatically. This automation was derived using two Convolutional Neural Network (CNN) models and images of solid waste were captured by the drone. Both models were trained on the collected image dataset at different learning rates, optimizers and epochs. This research uses symmetry during the sampling of garbage images. Homogeneity regarding resizing of images is generated due to the application of symmetry to extract their characteristics. The performance of two CNN models was evaluated with the state-of-the-art models using different performance evaluation metrics such as precision, recall, F1-score, and accuracy. The CNN1 model achieved better performance for automatic solid waste detection with 94% accuracy.

A completely new, intelligent μC-controlled arc detection system for air- and gas-insulated switchgear has been developed. This system's main features include high sensitivity, high operational reliability, high availability, zero maintenance, fault location with indication, along with self-monitoring (also covering the signal transmission route). High sensitivity is achieved by detecting the most striking and the fastest of physical variables indicative of an arc, i.e. its light. The light is detected by specially developed light sensor spheres facing each cladded compartment of the switchgear panel. These light sensor spheres display uniform sensitivity in all directions of the hemisphere. The light is conveyed by fibre-optics to an evaluator and converted into a digital signal by photodiodes and A/D converters. The signal is evaluated using an algorithm that incorporates auxiliary signals so as to permit unambiguous differentiation between an arc and any interference signals in order to achieve maximum reliability even where light interference signals are involved. The new arc detection system for type-tested medium-voltage switchgear makes it possible to minimize the potential duration of an arc and hence its destructive effect, resulting in enhanced safety for the switchgear environment. (5 pages)