International Research Journal of Engineering and Technology (IRJET) Volume: 09 Issue: 05 | May 2022
e-ISSN: 2395-0056
www.irjet.net
p-ISSN: 2395-0072
DETECTION AND EXTRACTION OF SEA MINE FEATURES USING CNN ARCHITECTURE Sheethal S, Soundarya S, Vidhisha V, Kiran G, Dr A B Rajendra Department of Information Science and Engineering VidyaVardhaka College of Engineering, Mysuru --------------------------------------------------------------------------***----------------------------------------------------------------------Abstract The Conventional worry of navel asset is naval mines; these mines are stationary and were planted during war times and now they have been working as a threat to naval ships, and submarines. Detection of those naval mines has been one of the foremost risk-taking tasks, with modern technology various techniques are wont to detect these mines Using Ultrasonic signals, Symbolic pattern analysis of side-scan sonar images but detection through image processing has been one of the most challenging and efficient ones since it can solve the real-time problem with less error, the image classification model like uses FRCNN(Fast Region Convolutional Neural Network) algorithm to classify the objects as mine or not. The cloud platform is employed to watch the mine and as soon as the changes are observed the Android application will reflect the changes.
Keywords: FRCNN, Neural network, Image processing, Deep Learning, ResNet, TensorFlow, Python 1. INTRODUCTION
RCNN uses FPN as the backbone. For searching items in regions, use RPN. Anchor boxes are utilized to align the point vector with the position in the raw image, which can then be compared to ground verity while discovering and utilizing the idea of IoU value.
Nonmilitary mines, often known as aquatic mines, are used in combat. During a conflict, mines are used to destroy naval assets. It's also used in the defense industry, where mines operate as a border to protect the country's marine territory. These mines prevent hostile maritime assets from entering the unmarked territory. The enemy must search the entire region for mines. The opponent is forced to assault in an unmined place, where the defense is ready for a fight. Unlike the older mines, the ultramodern mines are detonated by pressing a button. The discovery of underwater mines is critical in ensuring that civilians are not endangered in any way. Mines aid in securing high-altitude defense bases and preventing the leakage of sensitive information. Battlegroups will be able to pinpoint the exact location of mines and avoid losses with the help of a reliable and cost-effective method. The neural network's operation is comparable to that of a mortal brain. It's used to show the relationship between data across a computer system. This artificial network is primarily based on Machine Literacy. If a dependable and cost-effective technology was utilized, battle groups would be able to find the exact location of mines and save lives. The neural network works in the same way that a human brain does. It's a diagram that depicts the connections between data in a computer system. Machine literacy is the foundation of the artificial network. Mask RCNN creates an offer about the region in the image where the object might be present and then constructs bounding boxes and masks at pixel position, as well as forecasts the object's class. For producing point vectors from raw pictures, Mask © 2022, IRJET
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2. LITERATURE REVIEW [1] The paper mentions the simplest way of implementing various deep learning techniques, hence the modifications need to be done to the techniques this could be the major challenge of the paper. In this paper modules and sub-modules used are CNN, Autoencoders, Deep Belief Networks, and GAN. This paper provides an overview of the simplest ways to implement target recognition and hence not very efficient. [2] In this paper, the author Huu-Thu Nguyen, Eon-Ho Lee 1, and Sejin Lee specified sonar sensor needs and challenges to auto-detect submerged human bodies underwater. Sonar images need to be tested at different levels of polarization and intensities, the target background must be considered as there will be scatterings and noises in the sonar image. The same model needs to be retrained on sonar images of various polarization and intensity. [3] Self-Supervised Learning of Pretext-Invariant Representations is to construct image representations that are semantically meaningful via PIRL (Pretext Invariant Representation Learning) that do not require semantic annotations for a large training set of images. To achieve the highest single crop top-1 accuracy of all |
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