In the distributed optoelectronic system, when the optoelectronic reconnaissance equipments cooperates to locate the target, it needs to use the azimuth and elevation information of the optoelectronic reconnaissance equipments, which usually has measurement errors. This paper proposes a distributed optoelectronic system collaborative positioning model with measurement errors, and analyzes the influence of the measurement errors of azimuth and elevation on the target positioning error, At the same time, the influence of the baseline between the optoelectronic reconnaissance equipment on the target positioning error is analyzed. the influence of the included angle between and the positioning lines on the target positioning error is analyzed. The modeling analysis shows that the smaller the measurement error of azimuth and elevation angle are, the smaller the target positioning error is; The longer the baseline is, the smaller the target positioning error is; The closer the included angle of the positioning line is to 4/π, the smaller the target positioning error is. It provides a basis for the selection of angle measuring sensors in the distributed optoelectronic system and the layout of optoelectronic reconnaissance equipment in the distributed optoelectronic system.
By combining artificial neural network with deep learning technology, convolution neural network is characterized by local perception, adaptive feature extraction and end-to-end application, etc., and it has been used in image recognition and target detection more and more in recent years. Problems are existing widely in the traditional safety helmet detection algorithm generally such as the severe background interference, complex computing, high time-complexity and largely fluctuant accuracy. A detective method for safety helmet based on deep convolution network was proposed in this paper, which first decoded the acquired video monitoring data for a number of YUV images, then to determine the detecting area in the image, and transfer the YUV component image in the detecting area to the RGB image data; then in which to determine the training set and detecting set; finally, based on the constructed convolution neural network model to compute and process to acquire the ultimate detective results.
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