lily-hust Goto Github PK

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Bio: Researching and developing algorithms for image enhancement, target extraction and modeling.

lily-hust's Projects

Explaining the differences between traditional image classification, object detection, semantic segmentation, and instance segmentation is best done visually. When performing traditional image classification our goal is to predict a set of labels to characterize the contents of an input image (top-left). Object detection builds on image classification, but this time allows us to localize each object in an image. The image is now characterized by: Bounding box (x, y)-coordinates for each object An associated class label for each bounding box.Instance segmentation algorithms, on the other hand, compute a pixel-wise mask for every object in the image, even if the objects are of the same class label (bottom-right). Here you can see that each of the cubes has their own unique color, implying that our instance segmentation algorithm not only localized each individual cube but predicted their boundaries as well.

mask_rcnn

Mask R-CNN modified to run on TensorFlow 2

mask_rcnn_arundo

This is a Mask-RCNN model application on drone image texture based instance segmentation. The target object would be the Arundo bunches in the drone image. The difficulty here is the objects are only characterized as the textures, while most instance segmentation scenarios are objects characterized by shape as well as color and texture. The training samples have been manually labeled carefully. It is expected to have more samples in the future as more flying tasks will be arranged every half year.