Deep neural networks (DNNs), the machine learning algorithms underpinning the functioning of large language models (LLMs) and ...

Deep reinforcement learning-based object detection approaches center around a pivotal concept: hierarchically scaling image segments that harbor more intricate details. Compared with the traditional ...

"This object is unlike anything we have seen before," lead study author Andy Wang, an astronomer at Curtin University in Perth, Australia, said in a statement.

This study investigated an object detection technique for lung cancer using the YOLO object detection model to train and validate the model and evaluate whether it can effectively detect the types of ...

Recent advancements in medical object detection (MOD) have been propelled by the rapid evolution of deep learning (DL) technologies, revolutionizing medical imaging and diagnostic workflows. This ...

In the proposed moving object detection framework, four main stages are involved, namely, pre-processing: histogram equalization technique; segmentation: weighted Otsu-based segmentation algorithm; ...

Update log 2018/9/18 - update all of recent papers and make some diagram about history of object detection using deep learning. 2018/9/26 - update codes of papers. (official and unofficial) ...

A paper list of object detection using deep learning. I wrote this page with reference to this survey paper and searching and searching.. Last updated: 2019/07/31 ...