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Related Experiment Video

Updated: Aug 25, 2025

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

Towards Lightweight Neural Networks for Garbage Object Detection.

Xinchen Cai¹, Feng Shuang¹, Xiangming Sun²

¹School of Electrical Engineering, Guangxi University, Nanning 530004, China.

Sensors (Basel, Switzerland)

|October 14, 2022

Summary

This summary is machine-generated.

A new lightweight deep learning model, YOLOG (YOLO for garbage detection), accurately classifies garbage in real-time on embedded devices. This model significantly improves upon existing object detection algorithms for effective garbage management and environmental protection.

Keywords:

dilated–deformable convolution garbage classification lightweight neural network object detection

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Area of Science:

Computer Science
Artificial Intelligence
Environmental Engineering

Background:

Garbage classification is crucial for environmental protection and resource conservation.
Existing object detection models often struggle with accuracy and real-time performance on embedded devices for garbage classification.

Purpose of the Study:

To design and develop a lightweight garbage object detection model for real-time classification on embedded devices.
To address the limitations of low accuracy and poor real-time performance in current garbage classification systems.

Main Methods:

Proposed YOLOG (YOLO for garbage detection), a lightweight model based on accurate local receptive field dilation.
Incorporated DCSPResNet with dilated-deformable convolution, simplified network structure, and new activation functions.
Trained and tested the model on a custom domestic garbage image dataset.

Main Results:

YOLOG achieved an AP0.5 of 94.58% and a computation of 6.05 Gflops.
Outperformed YOLOv3, YOLOv4, YOLOv4-Tiny, and YOLOv5s in comprehensive performance metrics.
Demonstrated high-speed and high-performance detection of domestic garbage on embedded devices.

Conclusions:

The proposed YOLOG model provides accurate and rapid domestic garbage detection.
Offers a strong foundation for future academic research and practical engineering applications in waste management.
Facilitates effective garbage classification and improved recycling rates.