In the realm of Tiny AI, we introduce “You Only Look at Interested Cells”(YOLIC), an efficient
method for object localization and classification on edge devices. Through seamlessly …

Object detection is one of the predominant and challenging problems in computer vision.
Over the decade, with the expeditious evolution of deep learning, researchers have …

Aerial image object detection has a wide range of application values in civilian or military
fields. Due to its unique high-altitude imaging viewpoint and the multiangle shooting …

We present a simple yet effective learning technique that significantly improves mAP of
YOLO object detectors without compromising their speed. During network training, we …

We present a new version of YOLO with better performance and extended with instance
segmentation called Poly-YOLO. Poly-YOLO builds on the original ideas of YOLOv3 and …

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 …

The objective of deep learning-based object detection is to accurately localize and
recognize objects of interest from images or videos using neural networks. However, the …

Object detection is an emerging and essential problem in recent years, which has been
widely applied in many aspects of daily life such as video surveillance, self-driving robots …

Object detection in real images is a challenging problem in computer vision. Despite several
advancements in detection and recognition techniques, robust and accurate localization of …

Detecting and recognizing objects are crucial steps in interpreting remote sensing images.
At present, deep learning methods are predominantly employed for detecting objects in …