Image retrieve model for whales and dolphins, based on deep learning algorithms, analyzes features such as body shape, size, color, and tails to accurately classify images of different species. The technology has potential applications for scientific research, conservation, and marine wildlife management. It can help authorities identify species at risk of ship collision and reduce entanglement in fishing gear. Deep learning relies on large datasets to develop effective image retrieval models. We propose a network detects dorsal fins of whales and dolphins via feature extraction and image retrieval techniques. Architecture allows for efficient identification and outperforms comparison approaches like ResNet50 and EfficientNet B3, as evaluated on a dataset.
KEYWORDS: Image segmentation, Education and training, Medical imaging, Performance modeling, Semantics, Image processing, Data modeling, 3D modeling, Network architectures, 3D image processing
Image segmentation as a crucial step in image processing and analysis, has important applications in video surveillance, medical detection and wafer detection, etc. Accurate and efficient image segmentation can bring great advantages and convenience to the realization of related tasks in these fields. In this paper, a 2.5D UNet network based on ConvNeXt is proposed to realize the image segmentation task based on the gastroscopy image dataset. The experimental results show that the proposed method has better segmentation performance than the UNet model based on ResNet50, UNet model based on EfficientNetB0, and UNET2.5D model based on EfficientNetB1.
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