Foreground detection is a significant area of study within the realm of computer vision and plays a crucial role in video-based applications. The Vibe algorithm is an efficient foreground detection method, and this paper proposes an optimized Vibe algorithm that incorporates fused edge information to address the issues of "ghosting" and noise. Initially, the edge information derived from the three-frame method is enhanced by the Canny operator. Subsequently, the OTSU algorithm is employed to filter out additional noise in the image, thereby enhancing the robustness of the algorithm. Finally, the optimized three-frame difference method is fused with the Vibe algorithm to eliminate the "ghosting" and achieve a complete foreground target. Experimental results demonstrate that the proposed algorithm effectively removes "ghosting" and noise, providing good real-time performance and robustness, thereby meeting the application requirements of real-time detection.
KEYWORDS: Carbon, Environmental monitoring, Carbon dioxide, Sensors, Data transmission, Data communications, Air quality, Data storage servers, Telecommunications, Data storage
At present, the monitoring of carbon emissions is the main basis for achieving China's carbon peak by 2030 and carbon neutral by 2060, and it is also the most effective and important tool for building ecological civilization and achieving carbon peak and carbon neutral goals in each country. With the advancement of technology and the widespread use of the IOT, accurate monitoring systems were once a hot topic of discussion. This paper addresses the problem of accurate monitoring and designs and implements an IoT-based CO2 multi-point monitoring system, monitoring of CO2 as well as CH4 emissions. The monitoring system consists of a data collection and transmission as well as a supervisory platform. The collection terminals use LoRa low-power wireless transmission network to transmit data such as CO2 and CH4 values in real time to the gateway node, which transmits the data to the server by WI-FI network. The web monitoring interface was designed to display the collected CO2, CH4 and terminal location information in real time and to store the data through a MySQL database. Experimental tests have shown that the system can accurately monitor the amount of CO2 data and send data quickly and steadily, enabling real-time monitoring of complex environmental information from remote locations.
As a common measuring instrument, water meters are widely used in places such as water and power plants and households. The manual reading mode of old-fashioned water meters requires a large amount of manual and time costs. With the continuous breakthroughs in deep learning theory, it has become possible to use convolutional neural networks to automatically read old-fashioned meters. In order to improve the accuracy of meter reading and exclude other interference factors from the dial, this article proposes an attention-based DB network to make the model pay more attention to the meter display box, and uses a CRNN network for water meter reading, which effectively improves recognition accuracy. The model was tested and evaluated on a dataset of old-fashioned water and power plant meter dials, and the experimental results show good performance in the recognition accuracy of single and whole string characters. In addition, we deployed the model on Aiot to implement a high-precision and stable meter reading system.
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