It is an advanced version of EfficientNet, which was the state of art object detection model in early 2019, EfficientNet was a baseline network created by Automl MNAS, it achieved state-of-the-art ...

Research on multi-object detection is becoming increasingly prominent in the field of object recognition because of breakthroughs in deep learning. Camera and LiDAR are sensor technologies utilized ...

Then the image outputs of this filter will be fed into the same Faster R-CNN model that we have been using so far. The intent of this experiment is to examine how Faster R-CNN performs on ...

The developed moving object detection and classification using SAR images with an ensemble model is implemented in MATLAB, and the results are verified. Moreover, the ensemble method is computed over ...

Object detection and segmentation using the U-Net architecture, trained from scratch. This project demonstrates data preprocessing, training, and testing on the COCO 2017 dataset, showcasing ...

Foreign object detection (FOD) in railway catenary systems is crucial for ensuring operational safety and preventing catastrophic failures. However, current detection frameworks encounter two ...

The first step is to choose the right model for your object detection problem. There are many models available, such as Faster R-CNN, YOLO, SSD, and RetinaNet, each with different strengths and ...

DETR achieves results comparable to Faster R-CNN, an object detection model created primarily by Microsoft Research that’s earned nearly 10,000 citations since it was introduced in 2015 ...

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.