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- W4377700705 abstract "for machine vision to make sense of an image, object recognition techniques must first locate all of the items of interest within the frame of reference. Several strategies have been proposed to address this issue, many of which draw inspiration from computer vision and deep learning techniques. Existing methods, however, consistently underperform when trying to recognise small, dense objects and often fail altogether when trying to detect objects that have undergone random geometric alterations. In this research, we examine and compare state-of-the-art object detection techniques, then suggest a tweaked convolutional object detection network to address the problems with the status quo.Our investigation shows superior performance than current best practices. For this purpose, we employ deep convolutional networks to collect multi-scaled features, and we tweak the convolutional structures to account for geo-metric transformations.." @default.
- W4377700705 created "2023-05-24" @default.
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- W4377700705 creator A5066468645 @default.
- W4377700705 date "2023-04-07" @default.
- W4377700705 modified "2023-10-17" @default.
- W4377700705 title "Object Detection Algorithm based on Structural Modification in the Convolutional Neural Network for Remote Sensing Satellite İmages" @default.
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- W4377700705 doi "https://doi.org/10.1109/i2ct57861.2023.10126118" @default.
- W4377700705 hasPublicationYear "2023" @default.
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