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The importance of YOLO V3 in Object detection

3 years ago in Algorithms , Research Writing By Pranav

Algorithms

What is the importance of YOLO V3 in Object detection?

I have selected YOLO V3 as an object detection technique. I tried using the algorithm for various kinds of object detection and it worked for a few applications but lacks clarity while implementing the same in smaller object detection. I need assistance on how to detect smaller ships under complex backgrounds using YOLOv3?

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By Joshna Answered 3 years ago

YOLOv3 predicts both the boundary box and the class at the same time. It uses DarkNet and FPN (Feature pyramid networks) to make feature detection followed by convolutional layers. While DarkNet trains the YOLOv3 algorithm, Feature Pyramid Network is a feature extractor designed with a feature pyramid concept to improve accuracy and speed. Detecting objects in different scales is challenging, especially for detecting small objects. FPN enables the YOLOv3 algorithm to detect small ships under complex backgrounds such as clouds, waves and clutters.

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By Nirav Answered 3 years ago

If you are aiming at object detection, YOLO V3 can better assist you in the exact detection of target objects. This is because YOLO V3 has the advantage of utilizing the boundary box and the class at the same time. YOLO V3 helps in identifying even smaller objects irrespective of the different backgrounds.

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