相关实验视频

Updated: Jun 7, 2026

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

8.9K

DCN-YOLO:一个用于远程传感图像的小物体检测范式,利用扩展卷积网络进行远程传感

Meilin Xie1,2, Qiang Tang1,2, Yuan Tian1,2

  • 1University of Chinese Academy of Sciences, Beijing 100049, China.

Sensors (Basel, Switzerland)
|April 12, 2025
PubMed
概括

本研究介绍了DCN-YOLO,这是一种使用多尺度扩展卷积来改进远程传感图像中小物体识别的新型物体检测方法. 该方法增强了特征提取和上下文理解,以获得更好的准确性和稳定性.

关键词:
这是一个YOLO YOLO.背景聚合 背景聚合 背景聚合扩张的卷积性卷积.小物体检测检测

更多相关视频

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

436
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.3K

相关概念视频

Light Acquisition02:16

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...