This post is written to give a step-by-step description of how we will be implementing IR sensors and thermal imaging in our project.
The potential uses for infrared sensors, especially in cameras, are vast and include search and rescue, surveillance, crop and forest health, pipeline inspection, leak detection etc., depending upon the precision of the sensor. Infrared is not visible to the human eyes but one can probably feel it sometimes as radiated heat when its intensity is high. A thermal camera can hence detect areas of higher temperatures. It can reveal overheating sections of electrical equipment in various devices such as switch-gears and substations. A drone can help to detect these sections from a distance, making inspection safer than before. They can also be used for night vision and surveillance.
Gas Leakage Detection
During the gas leak due to significant speed of the gas extension, the temperature of the pipe around the crack is rapidly cooling. Compare to the normal temperature, the drop of temperature of the leak hole is according to expanding of the gas. This is because the frequency of the atomic collision of the gas is decreased.
The required data for this is obtained by recording using a thermal camera and saving the video in .avi format. After recording the video of the gas situation, the video is converted into an image in JPEG by using some simple Matlab codes. Hence we have a wide set of images now which are treated as a data set. Alternatively, images can be captured on the thermal camera and saved.
Image Enhancement is done to the photo in order to remove noise from the original image because most of the original image is unclear and blurred. The enhanced image is converted from RGB to Grayscale after which the image is enhanced by image filtering and noise reduction.
Image Thresholding- Referring to the image depicted in Fig. 4, the image (a) is the original image from the thermal camera. Image (b) is the filtered image that undergoes the rgb2gray process. Then, by using the threshold value that has been set, the coolest region of the image is detected (the blue color) as shown in Fig. 4(c).Then the image undergoes the binarization process to be easy in the analysis. Finally, the classifications of the image are based on the pixel value in the eroded image (d). The figure below shows the result of the color detection.
The arrows shown in Fig. 4 points the area that undergoes the thresholding process. The blue colors of the coolest region for the leakage are detected. After the coolest region was detected, the image is once again converted into a grayscale image in order to enhance the coolest region.
2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation-Gas Leakage Detection Using Thermal Imaging Technique by Mohd Shawal Jadin, Kamarul Hawari Ghazali