Under the background of intelligent technologies, art designers need to use information technology to assist the design of art factors and fully realize the integration of art design and information technology. Multisensor information fusion technology can more intuitively and visually carry out a more comprehensive grasp of the objectives to be designed, maximize the positive effects of art design, and achieve its overall optimization and can also help art designers get rid of the traditional monolithic and obsolete design concepts. Based on multisensor information fusion technology under wireless virtual reality environment, principles of signal acquisition and preprocessing, feature extraction, and fusion calculation, to analyze the information processing process of multisensor information fusion, conduct the model construction and performance evaluation for intelligent art design, and propose an intelligent art design model based on multisensor information fusion technology, we discuss the realization of multisensor information fusion algorithm in intelligent art design and finally carry out a simulation experiment and its result analysis by taking the environment design of a parent-child restaurant as an example. The study results show that using multisensor information fusion in the environmental design of parent-child restaurant is better than using a single sensor for that; at the same time, using force sensors has a better environmental design effect than using vibration sensors. The multisensor information fusion technology can automatically analyze the observation information of several sources obtained in time sequence under certain criteria and comprehensively perform information processing for the completion of the decision-making and estimation tasks required for intelligent art design.
Monitoring the performance of hybrid rice seeding is very important for the seedling production line to adjust the sowing amount of the seeding device. The objective of this paper was to develop a system for the real-time online monitoring of the performance of hybrid rice seeding based on embedded machine vision and machine learning technology. The embedded detection system captured images of pot trays that passed under the illuminant cabinet installed in the seedling production line. This paper proposed an algorithm for fixed threshold segmentation by analyzing the images with the exploratory analysis method. With the algorithm, the grid image and seed image were extracted from the pot tray image. The paper also proposed a method for obtaining pixel coordinates of gridlines from the grid image. Binary images of seeds were divided into small pieces, according to the pixel coordinates of gridlines. Each piece corresponded to a cell on the pot tray. By scanning the contours in each piece of the image to check whether there were seeds in the cell, the number of empty cells was counted and then used to calculate the missing rate of hybrid rice seeding. The seed number sowed in pot trays was monitored while using the machine learning approach. The experimental results demonstrated that it would consume 4.863 s for the device to process an image, which allowed for the detection of the missing rate and seed number in real-time at the rate of 500 trays per hour (7.2 s per tray). The average accuracy of the detection of missing rates of a seedling production line was 94.67%. The average accuracy of the detection of seed numbers was 95.68%.
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