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Manoj M is currently studying a Bachelor of Computer Science and Engineering at the RV College of Engineering, Bengaluru, India. He loves to work on problems which involve social cause. Manoj finds the field of Machine Learning quite interesting and would like to explore more.

He is working on including and extending the Machine Learning Library along with his professor. he says it is interesting and a challenging task.

Poster Abstract

Underwater mines pose extreme danger for ships and submarines. Therefore, navies around the world use mine countermeasure (MCM) units to protect against them. It consists of four stages: detection, classification, identification and disposal. The detection and classification steps are usually performed using a sonar mounted on a ship’s hull or on an underwater vehicle. After retrieving the sonar data, military personnel scan the Sea-Bed images to detect targets and classify them as mine-like objects (MLOs). To reduce the technical operator’s workload and decrease post-mission analysis time, Computer-Aided Detection and Automated Target Recognition Algorithms have been introduced.

Currently, Navies employ a range of different mine countermeasures that include the human factor. Navies’ primary target is to have fewer human lives involved, such as divers, in risky underwater operations of minefield detection. For this purpose, MCM units uses Autonomous Underwater Vehicles.

Previous generation mines needed physical contact or high frequency vibration of the ship to trigger an explosion. The newly developed mines, on the contrary, are equipped with sophisticated sensors, usually detecting some combinations of acoustic and magnetic signals. Some of them are smart mines equipped with artificial intelligence to detect any false signals that attempt to release them.

So, this area demands a lot of research to be made and modern technologies can be an aid to solve this. It demands the use of various Machine Learning Algorithms and this area has got a lot of Social Relevance.

The Algorithm most suited for mine detection is Convolutional Neural Network popularly known as CNN.

  • The convolutional Neural Network CNN works by getting an image, designating it some weightage based on the different objects of the image, and then distinguishing them from each other.
  • As we there are lots and lots of Big-data is available, HPCC Systems can be an aid to solve this problem effectively. 

About HPCC Systems and CNN:

  • The usage of CNNs is motivated by the fact that they can capture and are able to learn relevant features from an image at different levels similar to a human brain. The Conventional neural networks don’t have this feature.
  • For a completely new task, CNNs are very good feature extractors. It can extract useful attributes from an already trained CNN with its trained weights by feeding your data on each level and tune the CNN a bit for the specific task.
  • The model’s behavior and characteristics are highly dependent on the training dataset being used. Thus, it requires efficient means to clean, manage and filter the data and the services provided by HPCC Systems can be leveraged to create efficient and accurate model.


In this Video Recording, Manoj provides a tour and explanation of his poster content.

Underwater Mine Detection

Click on the poster for a larger image. 

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