To solve the problem of ship exhaust emission monitoring in Domestic Emission Control Areas (DECA), we present an Internet of Things (IoT)-based ship exhaust emission monitoring system in this paper. The system consists of three parts: a ship exhaust monitoring device installed above the lock, an IoT data platform, and a comprehensive display platform. The monitoring device uses an STM32 microcontroller as the Main Control Unit (MCU), obtains the concentration of various gases such as SO2 and CO2 in the smoke plume of the passing ships through gas sensors, and uploads real-time data to the ThingsBoard IoT data platform through the NB-IoT module. Finally, the fuel sulfur content (FSC) is calculated in the back end and is rendered to the front-end page. The test results show that the detection errors of SO2, CO2, and NO2 are - 0.7%, - 3.2%, and 3.7% respectively. The result proves that the system has high monitoring accuracy and can effectively improve the efficiency of ship emission supervision.
An infrared dim target detection method based on Human Visual System and Low Rank Matrix Factorization is proposed in this paper. Firstly, the sparse component of a small-target infrared image is obtained through fast matrix decomposition based on weighted scene priors, achieving preliminary screening of small target regions in the image. Then, utilizing prior knowledge of the shape and contrast distribution of small targets, a three-layer sliding window with oversampled sub-windows is applied to further suppress non-target areas in the sparse part image. Lastly, to accomplish accurate extraction of small targets, an adaptive threshold segmentation method is applied.The experimental results reveal that, while preservation of good real-time performance, the recommended approach outperforms traditional infrared small target identification procedures in terms of BSF and SCRG.
Addressing the limitations of currently rare small target detection algorithms based on Human Visual Systems (HVS) that struggle with achieving satisfactory performance in complex backgrounds and lack high real-time capabilities, this paper introduces an innovative small target detection algorithm. This approach is founded on the integration of the Full-Direction Dilation Difference of Gaussians (FDD-DOG) and background estimation based on local contrast (LCBE). First, the high-frequency signal in the target area is enhanced by using the FDD-DOG operation, based on the difference between the real target and the background. Then, the background noise is further suppressed by assigning appropriate weights to the enhanced image by using local contrast-based background estimation. After that, the target to be detected is extracted by the adaptive threshold segmentation method. Experiments comparing the algorithm proposed in this paper with other HVS-based detection algorithms show that the method in this paper exhibits better detection performance in different infrared image sequences, where the background suppression factor is improved by an average of about 25 times and the signal-to-clutter ratio gain is improved by an average of about 15 times.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.