The AttendSeg deep learning model performs semantic segmentation at an accuracy that is almost on-par with RefineNet while cutting down the number of parameters to 1.19 million.

Deep CNN based pixel-wise semantic segmentation model with >80% mIOU (mean Intersection Over Union). Trained on cityscapes dataset, which can be effectively implemented in self driving vehicle systems ...

Before the rise of deep learning, traditional machine learning techniques, such as model-based methods (e.g., active shape and appearance models) and atlas-based methods had been shown to achieve good ...

aerial-segmentation-> Learning Aerial Image Segmentation from Online Maps. IterativeSegmentation-> Recurrent Neural Networks to Correct Satellite Image Classification Maps. Detectron2 FPN + PointRend ...

Keywords: greenhouse image analysis, image segmentation, deep learning, U-net, quantitative plant phenotyping. Citation: Narisetti N, Henke M, Neumann K, Stolzenburg F, Altmann T and Gladilin E (2022) ...

Deep learning algorithms have depicted commendable performance in a variety of computer vision applications. However, training a robust deep neural network necessitates a large amount of labeled ...

A new technical paper titled “A Universal AI-Powered Segmentation Model for PCBA and Semiconductor” was published by researchers at Nordson Corporation. “This paper introduces a novel universal deep ...

I hope you have got a fair and understanding of image segmentation using the UNet model. Now you can try implementing image segmentation on different problems using -Net or by exploring other models ...