In this work, we attempt to illustrate possible applications of automated object detection in video sequences and still images using state-of-the-art artificial intelligence methods, such as convolutional neural networks. These novel tools can be used in various application domains in offshore operations. Neural networks can be trained to detect humans, specific objects and even learn how to conduct pipeline, anchor, tank and other types of inspections where they can significantly reduce the possibility of human error, not by replacing the human operator, but by helping him or her to be more effective and efficient. One of the problems we will discuss is how to harness the power of image pre-processing methods to increase the informative content of inputs, especially in environments where video or still-image visibility may be poor due to weather conditions, underwater turbidity or smoke.
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Virtual Multimodal Automated Object Detection with Deep Neural Networks
Paper presented at the SNAME 24th Offshore Symposium, Houston, Texas, February 2019.
Paper Number: SNAME-TOS-2019-016
Published: February 20 2019
Mitsakos, Nikolaos, Upadhyay, Sanat, and Manos Papadakis. "Virtual Multimodal Automated Object Detection with Deep Neural Networks." Paper presented at the SNAME 24th Offshore Symposium, Houston, Texas, February 2019.
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