YOLOv4 is an advanced and state-of-the-art object detection algorithm in the field of computer vision using artificial intelligence. It builds upon the success of its predecessors, YOLOv1, YOLOv2, and YOLOv3, and introduces several key enhancements to achieve even higher accuracy and faster processing speeds.
The algorithm approaches object detection as a single unified problem, simultaneously predicting bounding boxes and class probabilities for objects within an image. This unique one-shot approach sets YOLO apart from other object detection algorithms, as it eliminates the need for complex region proposal networks and post-processing steps.
YOLOv4 adopts the Darknet architecture, a lightweight neural network, as its backbone for feature extraction. It replaces traditional activation functions like ReLU with Mish activation, offering improved non-linearity and gradient flow, leading to enhanced model performance. Mosaic Data Augmentation combines multiple images into a single training sample, increasing the diversity of the training data.
For example, I used YOLOv4 algorithm to detect myself (green bounding box as human) in the live-camera feed.
In another example, I used YOLOv4 algorithm to detect various types of objects (as classes) from the photos captured by drone, as shown below. The identified objects are indicated by the bounding boxes. This instance can be deployed for a drone surveillance system.
YOLOv4 has significantly improved object detection performance, offering state-of-the-art accuracy while maintaining real-time processing speeds. It has been widely adopted in various applications, including autonomous vehicles, video surveillance systems, object tracking, and robotics. Overall, YOLOv4 represents a significant milestone in object detection, pushing the boundaries of accuracy and speed and contributing to advancements in computer vision technology.
Writer:
Md Nazmul Hasn Topu
PhD Researcher, Electrical & Computer Engineering University of British Columbia, Vancouver, Canada
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