Real Time object detection is a technique of detecting objects from video, there are many proposed network architecture that has been published over the years like we discussed EfficientDet in our ...

“Traditional object detection algorithms (YOLOv5, MobileNet SSD) are bad at this type of problem (similar-sized objects, lots of very small objects) so he designed a custom architecture that ...

The recent advances in compressing high-accuracy convolutional neural networks (CNNs) have witnessed remarkable progress in real-time object detection. To accelerate detection speed, lightweight ...

The proposed network achieves 98.88% grasp detection accuracy on the Cornell dataset and 95.23% on the Jacquard dataset. To further verify the validity, the grasping experiment is conducted on a ...

Roboflow has launched RF-DETR, an open-source real-time object detection model optimized for edge devices, enabling faster and more efficient AI vision applications.

It aims to enable object detection on microcontrollers in the power domain of milliwatts, with less than 0.5MB memory available for storing convolutional neural network (CNN) weights. The proposed ...

For each default box, the network predicts both the shape offsets and the confidences for all object categories [(c1, c2, …, cp)]. At training time, the default boxes are first matched to the ...