What’s new in YOLOv4?. YOLO is a real-time object recognition... | by Roman Orac | Towards Data Science

YOLO is short for You Only Look Once. It is a real-time object recognition system that can recognize multiple objects in a single frame. YOLO recognizes objects more precisely and faster than other recognition systems. It can predict up to 9000 classes and even unseen classes. The real-time recognition system will recognize multiple objects from an image and also make a boundary box around the object. It can be easily trained and deployed in a production system.
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YOLO is based on a single Convolutional Neural Network (CNN). The CNN divides an image into regions and then it predicts the boundary boxes and probabilities for each region. It simultaneously predicts multiple bounding boxes and probabilities for those classes. YOLO sees the entire image during training and test time so it implicitly encodes contextual information about classes as well as their appearance.
YOLO is developed by Joseph Redmon. The introduction of the YOLO real-time object recognition system in 2016 is the cornerstone of object recognition research. This led to better and faster Computer Vision algorithms.
YOLO v4 is developed by three developers Alexey Bochkovskiy, Chien-Yao Wang, and Hong-Yuan Mark Liao.
He quit developing YOLO v4 because of the potential misuse of his tech. He especially referring to “military applications and data protection issues”. He ceases his research for Computer Vision because he found that the ethical issues involved were “become impossible to ignore”.
YOLOv4’s architecture is composed of CSPDarknet53 as a backbone, spatial pyramid pooling additional module, PANet path-aggregation neck and YOLOv3 head.
CSPDarknet53 is a novel backbone that can enhance the learning capability of CNN. The spatial pyramid pooling block is added over CSPDarknet53 to increase the receptive field and separate out the most significant context features. Instead of Feature pyramid networks (FPN) for object detection used in YOLOv3, the PANet is used as the method for parameter aggregation for different detector levels.
YOLOv4 is twice as fast as EfficientDet (competitive recognition model) with comparable performance. In addition, AP (Average Precision) and FPS (Frames Per Second) increased by 10% and 12% compared to YOLOv3.
YOLO is a futuristic recognizer that has faster FPS and is more accurate than available detectors. The detector can be trained and used on a conventional GPU which enables widespread adoption. New features in YOLOv4 improve accuracy of the classifier and detector and may be used for other research projects.
YOLOv4: Optimal Speed and Accuracy of Object Detection
YOLO Is Back! Version 4 Boasts Improved Speed and Accuracy
YOLO Creator Joseph Redmon Stopped CV Research Due to Ethical Concerns
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