Corrigendum: Small target detection with remote sensing images based on an improved YOLOv5 algorithm

Related Experiment Video

Updated: Sep 26, 2026

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy
07:13

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

Published on: February 25, 2021

3.9K

Wenjing Pei1, Zhanhao Shi2, Kai Gong1

  • 1The Seventh Research Division and the Center for Information and Control, School of Automation Science and Electrical Engineering, Beihang University (BUAA), Beijing, China.

Frontiers in Neurorobotics
|March 17, 2023
PubMed

Abstract:

[This corrects the article DOI: 10.3389/fnbot.2022.1074862.].

Keywords:
EIoU lossYOLOv5sdeep learningremote sensing imagessmall target detection

More Related Videos

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

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

Published on: December 15, 2023

594
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

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

Published on: October 27, 2023

1.5K

Related Experiment Videos

Last Updated: Sep 26, 2026

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy
07:13

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

Published on: February 25, 2021

3.9K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

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

Published on: December 15, 2023

594
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

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

Published on: October 27, 2023

1.5K

Related Concept Videos

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...

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

Longitudinal joint trajectories of the dietary inflammatory index and physical activity and risk of incident cardiometabolic multimorbidity among women: evidence from the China Health and Nutrition Survey.

Frontiers in public health·2026

Flash Surface-Engineered Floating Proppants with Decoupled Density and Strength.

ACS nano·2026

Aqueous Adsorption of Rare Earth Elements by Organophosphonate-Ligand-Modified Metal-Organic Frameworks: The Role of Ligand Loading in Surface Complexation.

ACS applied materials & interfaces·2026

Research Progress of Multicomponent Co-Loaded Nanomedicine Delivery Systems for Tumor Immunotherapy.

International journal of nanomedicine·2026

Pharmacological activation of dopamine receptor D1 attenuates TGF-β-induced epithelial-mesenchymal transition in A549 and BEAS-2B cells.

Naunyn-Schmiedeberg's archives of pharmacology·2026

Triplet energy transfer competition in Mn2+-doped (Ph3S)2CdCl4 enables afterglow-green emission switching.

Optics letters·2026

Brain-inspired predictive visual attention for spatiotemporal semantic understanding of complex film and television videos.

Frontiers in neurorobotics·2026

Effectiveness of bimanual kinesthetic teaching through different controller settings.

Frontiers in neurorobotics·2026

Effect of robotic assistance on upper-limb sensorimotor recovery: a systematic review and meta-analysis.

Frontiers in neurorobotics·2026

Governing brain-computer interfaces as embodied autonomous neural systems: an ethical framework for neurorobotic governance in education.

Frontiers in neurorobotics·2026

Mechanonociceptive tactile feedback enhances prosthetic grasp control during repeated human-in-the-loop interaction.

Frontiers in neurorobotics·2026

Retraction: Intervention effect of rehabilitation robotic bed under machine learning combined with intensive motor training on stroke patients with hemiplegia.

Frontiers in neurorobotics·2026

An AOP-anchored framework integrating omics-derived potency and PBK simulations to support mechanistic chemical risk assessment.

Environment international·2026

Polylactic acid - and polybutylene adipate terephthalate-microplastics induced differential effects in Daphnia magna.

Aquatic toxicology (Amsterdam, Netherlands)·2026

miR-4652-5p targets GFPT1 to suppress ATF6-mediated unfolded protein response (UPR) and UPR-driven apoptosis to drive gastric tumor progression.

Cellular & molecular biology letters·2026

Targeting the ERO1A-PDIA1 redox axis in triple-negative breast cancer with AI-designed minibinders.

Cell death discovery·2026

Correction: Association between PM2.5 exposure and metabolic syndrome in older population and the mediating effect of amino acids.

Frontiers in public health·2026

Protecting workers from Algorithmic Management: a theoretical framework for four key protection factors.

Frontiers in public health·2026
See all related articles
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies
Jove
Visualize
Contact Us