Oil is one of the most important energy supplies for economic development. In recent years, the fire safety problems of petrochemical enterprises have become prominent, with serious casualties and property losses. The continuously monitoring of key areas through the low-cost and intelligent infrared thermal imaging video monitoring system has important engineering application significance for the improvement of petrochemical site safety problems. According to the characteristics of infrared thermal imaging fire target, this paper proposes a method of deep neural network combined with time-domain feature analysis to realize fire detection. Firstly, high thermal pixels are extracted from the infrared image, and the gray-scale image is converted into a binary gray-scale image. Based on the YOLOv4 tiny framework, multi-level channel prediction and attention mechanism are added to detect the fire candidate target of the binary image, Finally, the candidate target is finally determined by analyzing the time-domain characteristics. Compared with the traditional temperature threshold judgment infrared temperature measurement fire alarm system, it can achieve high detection rate and effectively reduce the false alarm rate of the system. The intelligent security monitoring system in Petrochemical area designed in this paper has been applied in practical engineering, and the fire detection effect is good, which realizes the requirements of low power consumption, low cost and high reliability of the security monitoring system in Petrochemical area based on infrared thermal imaging.
The petrochemical industry plays an active role in driving the growth and structural upgrading of the entire national economy. In the storage process of refined oil, personnel theft is an important factor causing economic losses. Using infrared thermal imaging technology to monitor the perimeter of the oil depot can effectively improve the level of security monitoring. According to the application requirements of personnel intrusion detection in oil storage areas, this paper studies the moving target detection method under the static platform, and adopts the improved ViBe moving foreground target detection method to effectively extract the moving foreground and effectively eliminate the small interfering targets. Kalman filter combined with Hungarian algorithm is used to track the moving target. The simulation results show that the algorithm can effectively achieve the effective trajectory prediction and tracking of the moving target. Finally, it is transplanted on the hisilic 3519v101 embedded platform to achieve the requirements of real-time detection.
In infrared imaging system, nonuniformity is the key factor limiting the improvement of imaging quality. In this paper, an adaptive neural network nonuniformity correction algorithm based on motion detection is proposed. The motion estimation algorithm based on gray projection is used to select the learning reference frame of neural network algorithm. Combined with edge detection algorithm and neighborhood variance information, the learning step size of neural network is adjusted adaptively, it effectively solves the “ghost effect” caused by insufficient scene motion in the traditional neural network algorithm. The algorithm automatically extracts effective image frames from the actual scene and updates the correction parameters, without planning the correction area and scene motion mode, and realizes automatic real-time correction.
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