The performance of intelligent vehicles (IVs) in object detection relies not only on the design
or scale of the CNN model they use but also on how effectively they share their acquired …

With the recent proliferation of Artificial Intelligence (AI), object detection is becoming
increasingly ubiquitous. It is one of the key features of Autonomous Driving Systems. In …

The advancement of Autonomous Vehicles (AVs) significantly relies on the integration of
Internet-of-Things technology for real-time data processing and decision-making. Object …

Federated Learning (FL) enables collaborative training of machine learning models for edge
devices (eg, mobile phones) over a network without revealing raw data of the participants …

Federated learning (FL) has great potential in visual applications such as object detection by
unmanned aerial vehicles (UAVs), since different UAVs can capture diverse characteristics …

Various computer vision techniques based on deep neural networks have been proposed to
detect objects accurately and fast. However, due to the privacy, security and communication …

Federated Learning (FL) has emerged as a potent framework for training models across
distributed data sources while maintaining data privacy. Nevertheless, it faces challenges …

One of the main challenges in designing deep learning networks for autonomous driving is
the lack of labeled data. Recent trends that address this problem involve the use of …

Visual object detection is a computer vision-based artificial intelligence (AI) technique which
has many practical applications (eg, fire hazard monitoring). However, due to privacy …

Object detection plays an essential role in many vehicular applications such as Advanced
Driver Assistance System (ADAS), Dynamic Map, and Obstacle Detection. However, object …