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Why is object detection more complicated than image classification?
Compared to Image Classification, Object Detection i s considerably more complicated due to the simple fact that an image can have anywhere from zero to dozens of objects in them. Which in turn, means that during training, an Object Detection model can output more than one prediction for a single image.
How to create your own object detection model?
The Tensorflow Object Detection API makes it easy to detect objects by using pretrained object detection models, as explained in my last article. In this article, we will go through the process of training your own object detector for whichever objects you like.
How are multiple objects detected in an image?
If multiple objects are present in an image, they will be detected in parallel due to the nature of convolution. If M objects are within a grid cell, this grid cell will perform M (5+N) convolutions.
How to detect more than one class of object?
The more different classes of object you want to detect the more classifiers and iterations you would need. Have a simple CNN such as Efficientnet B0 or B1 do the trick for you. Curate your training data such that you have 2 classes: single vs multi. And let the classifier take care of the business for you if localization is not required.
How to do object detection in computer vision?
Create a small window, crop the area within the window and pass it to the ConvNet. Slide the window further and keep it on passing its cropped content to the ConvNet. Once the window traveled over the whole image, increase the window size and repeat step 1 and step 2.
How is uolo used in image segmentation and object detection?
UOLO is a model that combines the segmentation module, UNet, and the object detection module, YOLOv2. The background of this idea is that the abstractions learned in the decoder layer of UNet contain multi-scale information that is useful not only for the segmentation of objects, but also for the detection of objects.