As airport facilities upgrade and the demand for air transportation increases, solving the problem of runway safety becomes particularly important. In order to ensure the safe takeoff and landing of aircraft, Foreign Object Debris (FOD) on the runway should be accurately and quickly detected during runway detection to reduce the probability of danger. At present, FOD detection of most airport runways still adopts the traditional manual inspection, which has low efficiency and high cost. An intelligent recognition system for vehicle-mounted FOD management based on YOLOv8 was proposed in this paper, applicable to real airport scenarios, and details the design of the system's core algorithm, emphasizing the YOLOv8 framework in the detection module. A new data enhancement algorithm is proposed to ensure the high performance of the system under complex environment changes. The experimental results show that the accuracy rate of the system is higher than 95%, and it demonstrates strong robustness in complex scenes.
In order to solve the problems of serious electromagnetic interference, high cost and long detection time of radar-optoelectronic traditional airport runways Foreign Objects Debris Detection(FOD) system, a novel assistant identification detection architecture was proposed which based on the photoelectric system without radar. Automatic recognition algorithm was present which based on yolov4, it is to improve the efficiency and accuracy of the FOD identification. The manual detection intervention of professional personnel was reduced, and the occurrence of missed detection and false detection was avoided. A novel data augmentation method is proposed to guarantee the performance of the system in the case of a small number of samples. The experimental results show that the system has been strong robustness in different complex scenes, the comprehensive detection rate of FOD is higher than 95%, and the recognition efficiency and accuracy are greatly improved compared with the traditional convolutional neural network(CNN).
In view of the problem of projection distortion in the result of aberration-free point test of off-axis paraboloid mirror, this paper analyzes its formation principle, puts forward a transformation method from the detection coordinate system to the processing coordinate system, according to the mathematical relationship between the two, reconstructs the surface shape of the detection result, realizes the transformation of projection distortion image, and analyzes the error of the transformation result by using the fucial function. It is proved that this method is feasible by using the reconstructed surface results to guide the NC Polishing.
In the international environment of violence, terrorist attacks and illegal drug smuggling, security issues are particularly important. Many companies and institutions in the world have successively developed safety inspection equipment, among which x-ray safety inspection equipment has been widely used. In order to ensure the safety of passengers, it is necessary to be able to accurately identify the knives, guns, inflammables, explosives and other dangerous articles in the luggage package during the security check, so as to reduce the probability of danger. At present, airport security inspectors need to change for half an hour, which is hard to work and easy to miss. An assistant identification system of contraband based on yolov4 to assist the security inspector to judge the X-ray image was proposed to improve the efficiency and accuracy of security inspection identification, reduce the manual detection intervention of professional training personnel, and avoid the occurrence of missed detection and false detection. A novel data augmentation method is proposed to guarantee the performance of the system in the case of a small number of samples. The experimental results show that the system has strong robustness in different channel directions and complex scenes, the comprehensive detection rate of lighter is higher than 95%, and the recognition efficiency and accuracy are greatly improved compared with the traditional convolutional neural network(CNN).
In order to solve the problems of serious electromagnetic interference, high cost and long detection time of radaroptoelectronic traditional airport runways Foreign Objects Debris Detection(FOD) system, a novel FOD detection algorithm was proposed which based on the improved yolov3. The multi-scale detection and feature extraction network were used to improve the learning ability of object features. Meanwhile, the classical image processing and the deep learning technology was adopt, the algorithm has the ability of target autonomous recognition, besides conventional image restoration. The experimental results show that the algorithm has a strong ability of autonomous recognition and environmental adaptability, the time consumption is better than 0.2s, which can meet the real-time detection of foreign objects debris. It has a guiding significance for the next stage of the engineering of pure-photoelectric foreign object detection system.
The traditional underwater laser communication system has less electro-optic conversion efficiency,
short lifetime, large volume, low reliability and complex communication systems. Meanwhile, Underwater
communication system with bandwidth above 1Mbps level is in urgent need. To solve these problems, a novel LED
optical communication system is present which based on full-duplex mode. In order to improve the communication
distance and reduce the influence of backscattering and reflection light, a novel method of blue light LED array placement
is proposed. A practical optical attenuation estimation algorithm based on different chlorophyll concentration is used to
evaluate the feasibility of 50m underwater communication link distance. The MATLAB numerical calculation are given.
The simulation results show that the design method of LED array light source proposed in this paper has small size, light
weight and simple structure, which can be applied to underwater optical communication in low channel attenuation, Mbps
level bandwidth and long distance(>50m).
Compared to coherent beam combining, incoherent beam combining can complete the output of high power laser beam with high efficiency, simple structure, low cost and high thermal damage resistance, and it is easy to realize in engineering. Higher target power is achieved by incoherent beam combination which using technology of multi-channel optical path correction. However, each channel forms a spot in the far field respectively, which cannot form higher laser power density with low overlap ratio of faculae. In order to improve the combat effectiveness of the system, it is necessary to overlap different faculae that improve the target energy density. Hence, a novel method for incoherent combining of far-field laser beams is present. The method compromises piezoelectric ceramic technology and evaluation algorithm of faculae coincidence degree which based on high precision multi-channel optical path correction. The results show that the faculae recognition algorithm is low-latency(less than 10ms), which can meet the needs of practical engineering. Furthermore, the real time focusing ability of far field faculae is improved which was beneficial to the engineering of high-energy laser weapon or other laser jamming systems.
The high-energy laser weapon is famous for its unique advantage of speed-of-light response which was considered as an ideal weapon against Unmanned Aerial Vehicle(UAV). However, due to the high energy laser reflection effect, the pixel gray distribution of the frame image will be changed drastically, and therefore the miss distance signal will be interfered strongly when the high energy laser irradiating on the UAV, which seriously affects precision of object tracking in practical application. The traditional “centroid method” or “template matching method” have been difficult to meet the requirements of high precision miss distance which was less than 1pixel(RMS) under the reflected light interfering. In order to developing operational effectiveness of weapon system, G-DS(Gray weighted factor-Diamond Search method) algorithm was proposed which combined with gray weighted factor based on self-learning mechanism. It has been studied for the characteristics of UAV images by field experiment. The results show that G-DS algorithm is low-latency(less than 5ms), which can reduce time complexity compared with the traditional ME algorithm, furthermore, G-DS algorithm was robust based on local motion vector of the block, which can improve ability of target detection and recognition compared with the traditional “centroid method” or “template matching method”. Hence, G-DS algorithm was beneficial to the engineering of high-energy laser weapon.
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