Object detection is one of the most challenging tasks in computer vision. With the advances
in semiconductor devices and chip technology, hardware accelerators have been widely …

Neural network models, celebrated for their outstanding scalability and computational
capabilities, have demonstrated remarkable performance across various fields such as …

You only look once (YOLO) is being used as the most popular object detection software in
many intelligent video applications due to its ease of use and high object detection …

With the increase in the use of unmanned aerial vehicles (UAVs)/drones, it is important to
detect and identify causes of failure in real time for proper recovery from a potential crash …

Sonar image object detection is essential in underwater rescue and resource exploration.
Although many convolution neural network (CNN)-based object detection algorithms have …

Executing deep neural networks (DNNs) on resource-constraint edge devices, such as
drones, offers low inference latency, high data privacy, and reduced network traffic …

A convolutional neural network-based multiobject detection and tracking algorithm can be
applied to vehicle detection and traffic flow statistics, thus enabling smart transportation …

This research paper presents an innovative methodology for the identification and detection
of objects in autonomous driving systems that employ field-programmable gate arrays …

The exponential increase in generated data as well as the advances in high-performance
computing has paved the way for the use of complex machine learning methods. Indeed, the …

In recent years, convolutional neural network (CNN)-based algorithms have been widely
used in remote sensing image processing and show tremendous performance in a variety of …