Gaussian Golden Search Optimization Based Support Vector Machine Model for Object Detection and Classification in Undersea Water Images
Keywords:
Seawater objects detection, Level Set Algorithm, Support Vector Machine, Gaussian Golden Search OptimizationAbstract
Underwater exploration is critical to the growth and usage of deep-sea assets, underwater autonomy is becoming increasingly vital in order to prevent the hazardous environment of deep sea. Intelligent computer vision is an especially essential component for underwater autonomous operation. Based on the underwater setting, poor light and poor-quality picture augmentation are required as a preprocessing method for aquatic vision. In this study, pre-process the original sea object images with homomorphic filtering to eliminate noise, improve contrast, and adjust the lighting. For segmenting the correct object of an image from the pre-processed image, utilize the level set model. Using the Gaussian Golden Search Optimization-based Support Vector Machine model (G2SO-SVM) technique, identify and categories underwater water images such as fish, corals, rocks, and urchins. MATLAB platform performs implementation and evaluation of the performance of proposed work employing various statistical parameters namely accuracy, specificity, sensitivity, and precision. The proposed work demonstrated higher detection and classification performances than previous state-of-art approaches
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