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A Dive Into Algorithms and Services for Object Detection and Recognition

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KT
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2 min read

Object detection and recognition are two primary tasks in computer vision that have experienced significant advancements in recent years, primarily driven by the development of Convolutional Neural Networks (CNNs). In this article, we'll delve into the algorithms and services used for these tasks, with a focus on those grounded in neural networks.

Object Detection

Object detection involves locating the presence and position of objects of a certain class within an image.

  • Region-Based Convolutional Neural Networks (R-CNNs): This algorithm divides the image into several regions and runs a CNN on each region. If an object is detected within a region, the region is labelled with the corresponding class.

  • Fast and Faster R-CNNs: These are improvements over the initial R-CNN algorithm. Fast R-CNN implements a region of interest pooling scheme which speeds up the algorithm, while Faster R-CNN replaces the selective search algorithm used in R-CNN with a region proposal network, further enhancing the speed.

  • YOLO (You Only Look Once): YOLO takes a different approach by applying a single neural network to the full image, dividing the image into regions, and predicting bounding boxes and probabilities for each region.

Object Recognition

Object recognition, or classification, involves predicting the class of an object in an image.

  • CNNs: CNNs have become the standard for image classification tasks. They consist of convolutional layers that automatically and adaptively learn spatial hierarchies of features.

  • ResNets (Residual Networks): ResNets introduced the concept of "skip connections" or "shortcuts" to allow the gradient to be directly backpropagated to earlier layers, greatly enhancing performance.

Services for Object Detection and Recognition

Apart from the DIY approach, there are several ready-to-use cloud-based services that offer object detection and recognition capabilities:

  • Amazon Rekognition: Provides powerful image and video analysis capabilities, including object and scene detection, facial analysis, and text detection.

  • Google Cloud Vision API: Allows developers to understand the content of an image by encapsulating powerful machine learning models.

  • Microsoft Azure Computer Vision API: Can analyze, describe, and tag image content, along with detecting objects and faces.

  • IBM Watson Visual Recognition: Classifies images, detects faces and food, and allows for custom classifiers.

Conclusion

Object detection and recognition have come a long way, with advances in neural networks playing a crucial role. Whether using an algorithmic approach or availing of the services offered by tech giants, developers can now build applications capable of understanding and interacting with the visual world in an unprecedented way.

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