Underwater object detection is vital for marine exploration, environmental monitoring, and military applications. However, the complexity of underwater environments poses significant challenges for ...

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 ...

One of the first steps to improve your object recognition models is to choose the right model architecture for your task. There are different types of models that can handle different levels of ...

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 ...

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 ...

Our proposed model leverages fully labeled base classes and quickly adapts to novel classes, using a meta feature learner and a reweighting module within a one-stage detection architecture. The ...

In this article, I will show you how I created an object detection model with the help of Google’s Teachable Machine in less than 5 minutes. Before going hands-on let us first understand the core, i.e ...

Additionally, a hybrid system has been developed that combines an object detection model with a segmentation model enhanced by RoadPainter. The results of this system enable the calculation of a ...