In this report, we present a fast and accurate object detection method dubbed DAMO-YOLO,
which achieves higher performance than the state-of-the-art YOLO series. DAMO-YOLO is …

In this paper, we aim to design an efficient real-time object detector that exceeds the YOLO
series and is easily extensible for many object recognition tasks such as instance …

Over the past years, YOLOs have emerged as the predominant paradigm in the field of real-
time object detection owing to their effective balance between computational cost and …

The better accuracy and efficiency trade-off has been a challenging problem in object
detection. In this work, we are dedicated to studying key optimizations and neural network …

In the past years, YOLO-series models have emerged as the leading approaches in the area
of real-time object detection. Many studies pushed up the baseline to a higher level by …

Real-time scene parsing through object detection running on an embedded device is very
challenging, due to limited memory and computing power of embedded devices. To deal …

Object detection is one of the main tasks of computer vision. Object detection algorithms
usually rely on deep convolutional neural networks, which require the host device to have …

An increasing need of running Convolutional Neural Network (CNN) models on mobile
devices with limited computing power and memory resource encourages studies on efficient …

The YOLO series has become the most popular framework for real-time object detection due
to its reasonable trade-off between speed and accuracy. However we observe that the …

To further improve the speed and accuracy of object detection, especially small targets and
occluded objects, a novel and efficient detector named YOLO-ACN is presented. The …