Shifted split-merge segmentation and fuzzy-guided generative adversarial network underwater object detection
Abstract
Marine object localization from UAVs is required for different areas, including marine research, environment monitoring and topographic surveys. Herein, we introduce a new undersea object detection method; synthesizing shifted split-merge segmentation and fuzzy-oriented generative adversarial networks. Split-merge segmentation is the region-based model, where this process is achieved via splitting and merging regions based on specified similarity measures. The previous split-merge segmentation algorithm was modified to employ a shifted window approach that is better at detecting undersea objects with changing shapes and sizes to address this issue. In addition to Guiding the segmentation quality of underwater object detection, a Fuzzy-Guided Generative Adversarial Network (FG-GAN) is proposed. The generator network aimed to produce artificially photographed images beneath the water, and the discriminator network was used to differentiate between real and fabricated pictures. The generator system is trained to use a fuzzy loss function with fuzzy membership functions to explain the level of uncertainty and vagueness in the underwater environment by controlling the behaviour of underwater entities. We positioned the above-described method and compared it to the traditionally used image partition and object detection approaches. The outcomes from these experiments indicate that our proposed method is more accurate than the existing approaches in segmenting the objects and identifying the objects accurately with 95% and has a reduced loss of 0.3. The proposed approach could be applied in a broad spectrum of underwater facilities such as marine hydrology, work with remote sensing equipment and underwater robotics.
Author
Saman M. Almufti
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