With the improvement of the level of social and economic development and the improvement of living standards, the amount of garbage output in China is increasing year by year, and the problem of garbage disposal is becoming more and more serious. In view of this situation, China began to carry out garbage classification. Garbage classification can give full play to the utilization value of resources and means of production and reduce the impact of human economic activities on nature. Therefore, aiming at the problems of difficult garbage classification and low efficiency of manual sorting, this paper designed and developed an intelligent garbage sorting vehicle. The system combines the background replacement algorithm to enhance the garbage data set, detects and classifies the garbage targets through the improved YOLOv5 network model, and finally realizes the garbage sorting work through the all-terrain intelligent vehicle equipped with mechanical arm. Through the test of garbage sorting in the real environment, the success rate of garbage identification by the intelligent garbage truck is 85%, which can meet the needs of garbage sorting in real life.
Non-destructive testing technology for large grinding wheel geometry is getting more and more attention from the industry. A device based on machine vision technology for intelligent measurement of large grinding wheel size is introduced. After calibrating and measuring the inside and outside radius of the grinding wheel and the thickness of the grinding wheel, intelligent detection is realized through a series of operations such as binarization of the original map, filling, expanding, outline extraction and outline coordinate extraction through hardware design and software programming. The hardware requirements of this design are simple. When measuring the radius of a grinding wheel, the method described in this paper gives the results of radius and height measurements with accuracy up to 5mm and 1mm, respectively. Finally, through repeated measurement experiments, the intelligent detection device of large grinding wheel size established in this paper can effectively solve the problems of field calibration of large grinding wheel and fast detection of inside and outside diameters.
The existing fire alarm system has strict distance and installation requirements between the fire point and the detector, and is easy to be interfered by environmental factors. It is not suitable for places with large space and many interference factors such as Climbazole production line. This paper proposed a flame image detection technology based on RGB+HSI color model and the detection system is designed and developed. The experimental results show that the flame image detection system based on RGB+HSI color model has the better recognition efficiency, which meets the real-time and accuracy requirements for early flame image detection in Climbazole production line.
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