Utilization of dense depth information for monoview object detection and instance segmentation

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2022-5-10

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Çakırgöz, Çağlayan Can

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Object detection aims for detecting objects of certain classes in an image by bounding them in rectangular boxes whereas instance segmentation tries to detect objects in pixel level. Deep learning techniques, which have shown great improvements over the last decade, are utilized in these topics as well, and a significant success is achieved against the traditional methods. Similar improvements can be observed in dense depth estimation which deals with deducing dense information of a scene from a single image. Previous works have shown that object detection and instance segmentation performances can be improved by incorporating sensor depth information. This thesis studies whether or not it is possible to have similar improvements when depth information is estimated from images instead of directly provided from sensors. Our research have shown that incorporating estimated depth data results in higher performance in object detection, although it fails in instance segmentation.

Subject Keywords

Object detection, Instance segmentation, Convolutional neural networks

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https://hdl.handle.net/11511/97381

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Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

Ç. C. Çakırgöz, “Utilization of dense depth information for monoview object detection and instance segmentation,” M.S. - Master of Science, Middle East Technical University, 2022.