Marine-Fouling Composition Estimation using Cost-effective Sensing

Determination of coverage and thickness of marine growth is a useful tool for determining structural loads and drags on marine structures and ships. In this work, we present an algorithmic program based on sonar and optical camera measurements, that estimates both the coverage and thickness of marine-fouling on off-shore structures. The marine-fouling composition is estimated using a Deep-Neural Network, trained using supervised methods, which can distinguish between hard/soft fouling species and the background water and structural components. The marine-fouling thickness is estimated using an HF Forward Looking Sonar, which is applied as a sensitive ultrasonic thickness gauge, when combined with a thickness measurement algorithm. Combined the measurements provide a localized estimate of the marine-fouling coverage and loadings across the structural surfaces, which can be used for automatic inspection evaluation and mission planning.