No abstract

No abstract

 Abstract: Corrosion is a prevalent issue in the oil and gas industry. Usually, pipelines made of Iron are used for oil and gas transportation. The pipelines are large and distributed over big fields above the ground, underground and even underwater. Corrosion gets developed because of environmental variables such as temperature, humidity and acidic nature of the liquids. There are different techniques for detecting and monitoring corrosion development, both destructive and non-destructive. Visual inspection is a common technique of surface corrosion analysis, but manual inspection is extremely dependent on the inspecting person's abilities and expertise. The findings of the manual inspection are qualitative and may be biased, may result into the accidents because of incorrect analysis. Corrosion must be accurately detected in early phases to prevent unwanted accidents. This paper will present a computer vision-based approach in combination with deep learning for corrosion classification as perISO-8501 standard. The findings of the assessment are unbiased and in a fair acceptable range similar to the outcomes of the visual inspection.

With the increase in crime and terror rate globally, automated video surveillance, is the need of the hour. Surveillance along with the detection and tracking has become extremely important. Human detection and tracking is ideal, but the random nature of human movement makes it extremely difficult to track and classify as suspicious activities. The primary objective of this is to detect the suspiciously abandoned object recorded by the closed-circuit television cameras (CCTV). The main aim of this project is to ease the load on the controller at the main CCTV station by generating and alarm, whenever there is a detection of an abandoned object. To solve the problem, we first proceeded by the background subtraction such that we obtain the foreground image. Further, we calculated the inter-pixel distance and used area-based thresholding so as to differentiate between the person and the object. The object will further be tracked for a previously set time, which will help the system to decide whether or not the object is abandoned or not. Such a system that can ease the load on single CCTV controller can be deployed in places which require high discipline and security and are more prone to suspicious activities like Airports, Metro station, Railway Stations, entrances and exits of buildings, ATMs, and similar public places.

No abstract