The features in an image are some unique points and edges. If you want to learn more about segmentation, you can follow this tutorial.Įach object class can be classified based on its features. You can see from the above image that, the object detection algorithm draws a ‘ bounding box‘ over the object, this technique will extract the exact object shape from the object. Instance Segmentation: Instead of detecting objecting and drawing bounding boxes, the instance segmentation algorithms can extract the actual object from the image. You can able to see an example of object detection in the above diagram. The object detector can draw a box around the detected object called ‘ bounding box‘. The object detection techniques are dealing with multiple object classification and it’s localization.
Fig (b): Object detectionįor example, in self-driving cars, it has to detect various kinds of vehicles on the road, pedestrians, road signs, road signals, etc. In a real-life scenario, we may have to find multiple objects from an image and its position. Object detection: The above two methods only cares about one object and its location.
This technique basically answers “ What is in the picture and where it is?“. Object localization: This method can predict the probability of an object in the image along with its location in the image. It can only predict one category for one image.
Object classification: This technique predicts the probability of different object categories( car, dog, cat, etc.) in an image, it essentially answers the question “ What is in the picture?”. What is Object detection? Fig (a): Object detectionīefore discussing the object detection concepts, it will be good to start with the following concepts in computer vision. Testing YOLO v4 in NVIDIA Jetson Nano board.YOLO v4: Testing video of YOLO v4 on Ubuntu.What are the different versions of YOLO?.
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