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

Parallel Processing

Parallel Processing

The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...

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...

Force Classification

Force Classification

Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...

Reducing Line Loss

Reducing Line Loss

In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...

Cross Product

Cross Product

The cross product is a fundamental concept in vector algebra that is a vector operation on two different vectors to obtain a third vector. Unlike the scalar product, the cross product results in a vector quantity perpendicular to both the original vectors.
The magnitude of the cross product is obtained by multiplying the magnitude of both the vectors and the sine of the angle between them. This means that a larger angle between the vectors will lead to a greater magnitude of the cross product.

You might also read

Related Articles

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

Sort by

Same author

Making Waves: Bridging the scale gap in aquatic phosphorus cycling.

Water research·2026

Same author

SpecBEV: An End-to-End BEV 3D Object Detection Algorithm Based on Frequency-Domain Analysis and Geometric Alignment.

Sensors (Basel, Switzerland)·2026

Same author

Enhancing landslide susceptibility mapping analysis through neighborhood feature aggregation and interpretable machine learning: Implications for disaster prevention in valley-type urban planning.

Journal of environmental management·2026

Same author

Rhythmic radial oxygen loss enhances soil phosphorus bioavailability.

Nature communications·2025

Same author

Multidirectional Fate Path Model to Connect Phosphorus Emissions with Freshwater Eutrophication Potential.

Environmental science & technology·2024

Same author

The underestimated importance of fertilizer in aquacultural phosphorus budget: Case of Chinese mitten crab.

The Science of the total environment·2022

Search research articles

Related Experiment Videos

LPCF-YOLO: A YOLO-Based Lightweight Algorithm for Pedestrian Anomaly Detection with Parallel Cross-Fusion.

Peiyi Jia¹, Hu Sheng¹, Shijie Jia¹

¹School of Rail Intelligent Engineering, Dalian Jiaotong University, Dalian 116028, China.

Sensors (Basel, Switzerland)

|May 14, 2025

Summary

This study introduces LPCF-YOLO, a lightweight pedestrian anomaly detection network. It significantly reduces model complexity and computational cost while maintaining or improving detection accuracy for real-world applications.

Keywords:

LPCF-YOLO lightweight feature extraction parallel cross-fusion pedestrian abnormal detection

Related Experiment Videos

Area of Science:

Computer Vision
Deep Learning
Artificial Intelligence

Background:

Current pedestrian anomaly detection networks are often too complex for practical deployment.
High computational requirements hinder the real-world application of advanced detection models.

Purpose of the Study:

To develop a lightweight anomaly detection network for efficient pedestrian monitoring.
To reduce the parameter count and computational load of existing models without sacrificing performance.

Main Methods:

Proposed LPCF-YOLO (Lightweight Parallel Cross-Fusion YOLO) based on YOLOv8n.
Introduced FPC-F and S-EMCP modules in the backbone, and an ADown module for cost reduction.
Implemented a Lightweight High-level Screening Feature Pyramid Network (L-HSFPN) in the neck.
Utilized Wise-IoU loss for improved localization and generalization.

Main Results:

Reduced parameters by 30.33% and FLOPs by 79.01% compared to YOLOv8n.
Achieved 2.09 M parameters and 1.7 G FLOPs, with a 179.62% increase in FPS to 43.9.
Maintained or slightly improved mean average precision (mAP@0.5) on UCSD-Ped1 and UCSD-Ped2 datasets.

Conclusions:

LPCF-YOLO offers a significantly more efficient alternative for pedestrian anomaly detection.
The proposed lightweight architecture enables practical real-world deployment of anomaly detection systems.