Image Segmentation for Object Detection using Mask R-CNN in Colab

Image segmentation is a critical process in computer vision. It involves dividing a visual input into segments to simplify image analysis. Segments represent objects or parts of objects, and comprise sets of pixels, or “super-pixels. Recently, due to the success of deep learning models in a wide range of vision applications, there has been a substantial amount of works aimed at developing image segmentation approaches using deep learning models. Image segmentation with CNN involves feeding segments of an image as input to a convolutional neural network, which labels the pixels. Fully convolutional network, FCNs use convolutional layers to process varying input sizes and can work faster. The final output layer has a large receptive field and corresponds to the height and width of the image, while the number of channels corresponds to the number of classes. An architecture based on deep encoders and decoders, also known as semantic pixel-wise segmentation. It involves encoding the input image into low dimensions and then recovering it with orientation invariance capabilities in the decoder. Most notably is the R-CNN, or Region-Based Convolutional Neural Networks, and the most recent technique called Mask R-CNN that is capable of achieving state-of-the-art results on a range of object detection tasks.