This application integrates YOLOv3 and PolyRNN++ for comprehensive image analysis. The App and Demo: Try the App!! https://objsegment-a5090a8bb17f.herokuapp.com/ https://www.youtube.com/watch?v=ZneiGaACYN4&embedable=true Why Image annotations? Image annotation is the process of labeling or adding metadata to images to provide additional information about the contents of the images. It involves the process of labeling images with relevant information, such as object boundaries, semantic segments, or object classes, to train these models effectively. It plays a crucial role in enabling machine learning models to understand and interpret visual data. However, the task of image annotation is not only laborious but also time-consuming, often requiring significant human effort to annotate each image accurately. Annotating a single object within an image can take up to 40-60 seconds, highlighting the challenges and costs associated with this process. The market for data collection and labeling is growing rapidly, with a projected value of . $2.82 billion by 2023 To address these challenges, the objective of this project is to automate the semantic segmentation process as much as possible, aiming to minimize the manual labeling effort required. By implementing a annotation tool, the aim is to develop a system that can accurately detect and segment objects within images automatically. Such an automated system would not only reduce the burden on human annotators but also improve efficiency and potentially reduce errors in the annotation process. deep learning-based Object Detection Object detection entails the identification and localization of objects of interest within an image or video. Its primary objective is not only to classify the objects present in the image but also to determine their exact spatial coordinates, generally in the form of bounding boxes. There are various model architectures that have been developed for object detection, each with its own strengths and limitations. A few of the commonly used architectures: RCNN — Paper & Code Fast-RCNN — Paper Faster-RCNN — Paper YOLO — Paper (You only look once) is a single-shot object detection algorithm, i.e., a single neural network that predicts bounding boxes and class probabilities directly from full images in one evaluation. This unified model enables faster predictions, and unlike sliding window techniques, YOLO considers the entire image and simplicity encodes contextual information about the classes and their appearance YOLO . YOLO models object detection as a regression problem and works as follows: Divides the image into an SxS grid. For each grid cell, target variable y is: a) B bounding boxes represented by the center of the bounding box relative to the grid cell ( x, y coordinates), width (w), and height of the box (h) with respect to the whole image. b) Confidence for those boxes, which reflects how confident the model is that the box contains an object and how accurately the box has predicted the object boundaries. Confidence = Pr(Object)*IOU. c) C class probabilities: C class probabilities P (Class I | Object). The overall target (y) for the image becomes an SxS(5B + C) tensor. Train a convolution network with input as an image and output with a tensor of the dimensions y. The output of the final layers then goes through that selects a single bounding box out of many overlapping bounding boxes. Non Maximum Suppression a) Discard low probability predictions. b) Compute the IOU of boxes; for boxes with IOU ≥ 0.5, retain the one with the highest probability. In summary, YOLO’s innovative approach to object detection has made it a go-to choice for real-time applications. Its ability to handle multiple objects in a single pass, coupled with its high accuracy and speed, has made it an indispensable tool in the field of computer vision. In recent years, YOLO has continued to evolve, with versions like YOLOv2, YOLOv3, and YOLOv4 addressing various challenges and improving object detection performance even further as the field of computer vision advances, YOLO and similar algorithms are likely to play an increasingly critical role in shaping the future of AI-driven applications. Semantic Segmentation Semantic segmentation divides the image into distinct regions based on the semantic meaning of the objects within it. When it comes to representing the segmentation, there are two primary options: a pixel-by-pixel approach, where each pixel is classified as foreground or background, or a sparse polygon representation that outlines the object boundaries. There are various model architectures that have been developed for semantic segmentation, each with its own strengths and limitations. Few of the commonly used architectures: U-NET — Paper & Code Mask-RCNN — Paper Polygon-RNN — Paper and its variants comprise two key modules. The first module focuses on the task of image representation and the extraction of image features. In this module, a Convolutional Neural Network (CNN) is employed, specifically utilizing the VGG network architecture without a dense layer. To enhance the information flow, skip connections are incorporated at the top of the network, enabling the fusion of information originating from different levels of the CNN. This fusion is crucial as it combines both low-level details, which are essential for identifying edges and boundaries, and high-level features, which play a pivotal role in recognizing objects within the image. This processed image representation is then passed into a Recurrent Neural Network (RNN), which employs Convolutional Long Short-Term Memory (ConvLSTM) units. The role of this RNN is to generate a sequence of vertices that collectively define the object of interest in the image, effectively providing a structured and accurate representation of the object’s shape. Polygon RNN Polygon RNN’s ability to generate polygonal representations offers a valuable advantage, especially when dealing with complex object shapes and fine-grained segmentation tasks. By using sequences of vertices to outline object boundaries, it strikes a balance between computational efficiency and segmentation accuracy. In conclusion, semantic segmentation plays a pivotal role in computer vision applications by providing a detailed understanding of image content. Model architectures like Polygon RNN and its variants showcase the innovation in this field, enabling the accurate delineation of object boundaries. As research in semantic segmentation continues to advance, we can expect more sophisticated techniques and models that further enhance our ability to extract meaningful information from images, opening up new possibilities for AI-driven applications across various domains. References: Yolo v3 Architecture — https://towardsdatascience.com/yolo-v3-explained-ff5b850390f Redmon, Joseph, et al. "You only look once: Unified, real-time object detection." . 2016. Proceedings of the IEEE conference on computer vision and pattern recognition Poly-RNN++ Architecure — https://arxiv.org/pdf/1704.05548.pdf ADE20K Dataset — https://groups.csail.mit.edu/vision/datasets/ADE20K/ Also published . here

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