Fouling control coatings (FCCs) are used to prevent the accumulation of marine biofouling on, e.g., ship hulls, which causes increased fuel consumption and the global spread of non-indigenous species. The standards for performance evaluations of FCCs rely on visual inspections, which induce a degree of subjectivity. The use of RGB images for objective evaluations has already received interest from several authors, but the limited acquired information restricts detailed analyses class-wise. This study demonstrates that hyperspectral imaging (HSI) expands the specificity of biofouling assessments of FCCs by capturing distinguishing spectral features. We developed a staring-type hyperspectral imager using a liquid crystal tunable filter as the wavelength selective element. A novel light-emitting diode illumination system with high and uniform irradiance was designed to compensate for the low-filter transmittance. A spectral library was created from reflectance-calibrated optical signatures of representative biofouling species and coated panels. We trained a neural network on the annotated library to assign a class to each pixel. The model was evaluated on an artificially generated target, and global accuracy of 95% was estimated. The classifier was tested on coated panels (exposed at the CoaST Maritime Test Centre) with visible intergrown biofouling. The segmentation results were used to determine the coverage percentage per class. Although a detailed taxonomic description might be complex due to spectral similarities among groups, these results demonstrate the feasibility of HSI for repeatable and quantifiable biofouling detection on coated surfaces.
Keywords: biofouling; classification; fouling control coatings; hyperspectral imaging; led illumination; pixelwise calibration; spectral library.