Machine Learning based Underwater Mine Detection

Underwater mining of minerals and rocks is a highly challenging task before the discovery of SONAR (Sound Navigation and Ranging) system. Lately, the mine detection process was performed by the divers trained in the disposal of hazardous ordnance, marine mammals, video cameras mounted on mine-neutralization trucks, and laser systems. which leads to risk and loss to the marine life. SONAR system is capable of capturing Scan-side sonar images, but the model’s accuracy is a concern. So Naval defense system need to use a much more accurate system as mines can be easily mistaken as rock, to obtain accurate results we will be working on the dataset of frequencies. Recently, this prediction system was constructed using many machine learning methodologies. This research study proposes to apply XGBoost algorithm to develop a prediction system to predict whether the object is rock or mine. Here, the accuracy of the proposed model is compared with the accuracy of the existing models.