Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Difference from Background: Limit of Detection

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...

Depth Perception and Spatial Vision

Depth Perception and Spatial Vision

Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.

Uniform Depth Channel Flow: Problem Solving

Uniform Depth Channel Flow: Problem Solving

To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...

Shape and Texture of Coarse Aggregate

Shape and Texture of Coarse Aggregate

Aggregate shape is classified based on the relative sharpness or roundness of the edges and corners. This classification includes categories like rounded, angular, elongated, and flaky, each with specific characteristics. Rounded aggregates, fully shaped by attrition, are typical of river or seashore gravel, while angular aggregates, such as crushed rock, have well-defined edges. Aggregates that are elongated and flaky are less desirable, as they can reduce the workability and strength of...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Switchable Ultralong Chiral Signal Transmission and Gate Tunability in Organic Chiral Semiconductor.

Research (Washington, D.C.)·2026

Same author

Tirofiban for Reduction of TEAR: A Phase 2, Randomized, Open-Label, Blinded End Point, Controlled Trial.

Stroke·2026

Same author

Minimally invasive treatment of emphysematous pyelonephritis in diabetic patients: a comparative study.

BMC urology·2026

Same author

Site-specific biomechanical alterations of the knee during gait in ACL-deficient patients with concomitant cartilage lesions.

Frontiers in bioengineering and biotechnology·2026

Same author

Origin of Suppressed Photovoltage Loss in Organic Solar Cells With Additive Engineering.

Angewandte Chemie (International ed. in English)·2026

Same author

Neutrophil-lifecycle-inspired nanoplatform for the treatment of lung cancer bone metastasis.

Nanoscale·2026

Search research articles

Related Experiment Video

Updated: Sep 1, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Texture and semantic integrated small objects detection in foggy scenes.

Zhengyun Fang¹, Hongbin Wang^2,3, Shilin Li⁴

¹College of Land Resource Engineering, Kunming Universityof Science and Technology, Kunming, Yunnan, China.

|August 18, 2022

Summary

This summary is machine-generated.

This study introduces a new method for detecting small objects in foggy conditions by integrating texture and semantic information. The approach improves feature extraction, enhancing detection accuracy in adverse weather.

More Related Videos

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

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

Published on: December 15, 2023

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Related Experiment Videos

Last Updated: Sep 1, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

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

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

Published on: December 15, 2023

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Area of Science:

Computer Vision
Artificial Intelligence
Image Processing

Background:

Small object detection is crucial but challenging in foggy environments.
Existing methods struggle with feature extraction in adverse weather conditions.
Lack of texture and high-level semantic information limits performance.

Purpose of the Study:

To propose a novel algorithm for small object detection in foggy scenes.
To integrate texture and semantic information for improved feature extraction.
To enhance detection performance in adverse environmental conditions.

Main Methods:

A knowledge guidance module uses clear image features to guide foggy image learning.
Adversarial learning extracts texture information from low-resolution images.
An attention mechanism enhances high-level semantic information across feature scales.

Main Results:

Achieved 46.2% mean average precision (mAP) on the "Cityscape to Foggy" dataset.
Achieved 33.3% mAP on the "CoCo" dataset.
Demonstrated significant improvement over existing methods.

Conclusions:

The proposed texture and semantic integrated method is effective for small object detection in fog.
The approach successfully extracts discriminative features unaffected by environmental conditions.
The method enhances the ability to obtain texture and semantic information for tiny objects.