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Deep Neural Network Driven Automated Underwater Object Detection

Computers, Materials & Continua 2022年第3期70卷 5251-5267页

作者： Ajisha Mathias Samiappan Dhanalakshmi R.Kumar R.Narayanamoorthi Department of Electronics and Communication Engineering SRMInstitute of Science and TechnologyKattankulathurChennai603203India Department of Electrical and Electronics Engineering SRMInstitute of Science and TechnologyKattankulathurChennai603203India

Object recognition and computer vision techniques for automated object identification are attracting marine biologist’s interest as a quicker and easier tool for estimating the fish abundance in marine ***,the biggest problem posed by unrestricted aquatic imaging is low luminance,turbidity,background ambiguity,and context camouflage,which make traditional approaches rely on their efficiency due to inaccurate detection or elevated false-positive *** address these challenges,we suggest a systemic approach to merge visual features and Gaussian mixture models with You Only Look Once(YOLOv3)deep network,a coherent strategy for recognizing fish in challenging underwater *** an image restoration phase,pre-processing based on diffraction correction is primarily applied to *** YOLOv3 based object recognition system is used to identify fish *** objects in the background that are camouflaged are often overlooked by the YOLOv3 model.A proposed Bi-dimensional Empirical Mode Decomposition(BEMD)algorithm,adapted by Gaussian mixture models,and integrating the results of YOLOv3 improves detection efficiency of the proposed automated underwater object detection *** proposed approach was tested on four challenging video datasets,the Life Cross Language Evaluation Forum(CLEF)benchmark from the F4K data repository,the University of Western Australia(UWA)dataset,the bubble vision dataset and theDeepFish *** accuracy for fish identification is 98.5 percent,96.77 percent,97.99 percent and 95.3 percent respectively for the various datasets which demonstrate the feasibility of our proposed automated underwater object detection method.

关键词： Underwater images diffraction correction marine object recognition gaussian mixture model image restoration YOLO

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