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A similarity-guided segmentation model for garbage detection under road scene.

Caiyun Zheng¹, Danhua Cao², Cheng Hu¹

¹School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, 430074, China.

Frontiers of Optoelectronics

|January 13, 2023

Summary

This study presents an efficient deep learning model for road garbage segmentation, achieving high accuracy and speed. The model uses feature pyramid attention and similarity guidance for improved urban street cleaning and reduced energy waste.

Keywords:

Garbage segmentation Machine vision Semantic segmentation

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Area of Science:

Computer Vision
Deep Learning
Artificial Intelligence

Background:

Urban street cleaning generates significant energy waste.
Intelligent control of road sweepers is needed to optimize efficiency.
Accurate garbage segmentation is crucial for automated cleaning systems.

Purpose of the Study:

To develop an efficient deep learning model for segmenting seven categories of road garbage.
To improve the speed and accuracy of garbage detection for intelligent road sweepers.
To enable effective segmentation with limited annotated data.

Main Methods:

A lightweight deep learning model with a feature pyramid attention (FPA) module in the decoder for multi-level feature integration.
Incorporation of a similarity guidance (SG) module using metric learning to guide segmentation results.
Model parameters under 3 million, achieving over 65 FPS on an RTX 2070 GPU.

Main Results:

Achieved an overall mean intersection-over-union (mIoU) of 0.87 and 0.67 on two custom garbage datasets.
Demonstrated a competitive trade-off between speed and accuracy.
Enabled acceptable category-balanced segmentation with fewer than 20 annotated images per category when using the SG module.

Conclusions:

The proposed deep learning method offers an efficient solution for road garbage segmentation.
The model's lightweight design and advanced modules (FPA, SG) enhance performance and data efficiency.
This technology can contribute to reducing energy waste in urban street cleaning through intelligent road sweeper control.