Object detection and Object segmentation
Object detection and object segmentation are two distinct computer vision tasks that are commonly used in various applications such as self-driving cars, surveillance systems, and medical imaging. Both object detection and object segmentation are typically powered by deep learning algorithms, specifically Convolutional Neural Networks (CNNs).
Object Detection:
Object detection involves detecting the presence of objects in an image or a video frame, and determining their location and size. The goal of object detection is to identify and locate all instances of objects of a certain class in an image or video.The technology behind object detection involves training a CNN to identify objects within an image and also to predict the bounding box around the object. These algorithms typically use a two-stage approach, where in the first stage, the algorithm generates region proposals, or potential object locations, and in the second stage, it classifies each region proposal as an object or background.
Object detection algorithms typically use sliding windows or regions proposals to scan the image, and apply object classification on these regions. Common object detection algorithms include:
- R-CNN (Regions with Convolutional Neural Network) and its variants (Fast R-CNN, Faster R-CNN, etc.)
- YOLO (You Only Look Once)
- Single Shot MultiBox Detector (SSD)
- RetinaNet
Object Segmentation:
Object segmentation involves not only detecting the presence of objects in an image or video frame, but also segmenting the objects to separate them from the background.object segmentation involves training a CNN to predict a pixel-level mask for each object instance. This type of algorithm typically consists of two parts: a backbone network, which is used to extract features from the input image, and a decoder network, which takes the features from the backbone and generates the final segmentation mask.
Object segmentation algorithms try to identify and segment each instance of the object class in the image. The goal is to obtain a pixel-level mask for each object instance. Common object segmentation algorithms include:
- Mask R-CNN
- U-Net
- Fully Convolutional Networks (FCN)
- DeepLabv3+
In summary, the technology behind object detection and object segmentation is deep learning, specifically Convolutional Neural Networks (CNNs), which are trained on annotated datasets to learn to identify objects and predict their bounding boxes or masks.
#Object detection and Object segmentation
