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 …

In recent years, Convolutional Neural Networks (CNNs) have been widely applied in
computer vision and have achieved significant improvements in object detection tasks …

Deploying deep learning models on embedded systems for computer vision tasks has been
challenging due to limited compute resources and strict energy budgets. The majority of …

The advancement of object detection algorithms makes them widely used in autonomous
systems. However, due to high computational complexity of Convolutional Neural Networks …

Abstract Algorithms based on Convolutional Neural Network (CNN) have recently been
applied to object detection applications, greatly improving their performance. However …

Object detection has been widely adopted in video analysis and image understanding.
Anchor-based object detection has achieved good performance on the scale variation that is …

Deep neural network (DNN)-based object detection has been investigated and applied to
various real-time applications. However, it is hard to employ the DNNs in embedded …

In limited-resource edge computing circumstances such as on mobile devices, IoT devices,
and electric vehicles, the energy-efficient optimized convolutional neural network (CNN) …

Deep neural networks (DNNs), as the basis of object detection, will play a key role in the
development of future autonomous systems with full autonomy. The autonomous systems …

Tiny machine learning (TinyML) has become an emerging field according to the rapid
growth in the area of the internet of things (IoT). However, most deep learning algorithms are …