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Litter Detection from Digital Images Using Deep Learning

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Abstract

In order to achieve automatically litter detection in residential area, machine vision has been applied to monitor environment of surveillance. Based on our observations and comparative analysis of the current algorithms, we propose an improved object detection method based on Faster R-CNN algorithm and achieve more than 98% accuracy of litter detection in surveillance. Through our observations, most of litters are small objects, we apply feature pyramid network to Faster R-CNN and optimize it by merging different layers by using multiply operate. Besides, we replace cross-entropy loss function with focal loss function to solve the problem of anchor imbalance by using region proposal network (RPN) and offer attention module through RPN to feedback the whole network. We collected more than 8000 labeled images from our surveillance videos for model training. Our experiments show that the improved Faster R-CNN achieves a satisfied performance in real scene.

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No data was used for the research described in the article.

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Authors and Affiliations

  1. China Jiliang University, Hangzhou, China

    Jianfeng Liu & Chen Pan

  2. Auckland University of Technology, Auckland, New Zealand

    Wei Qi Yan

Authors
  1. Jianfeng Liu
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  2. Chen Pan
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Corresponding author

Correspondence to Wei Qi Yan.

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This article is part of the topical collection “From Geometry to Vision: The Methods for Solving Visual Problems” guest edited by Wei Qi Yan, Harvey Ho, Minh Nguyen and Zhixun Su.

Appendix

Appendix

The notation or abbreviation table for the symbols

CE loss

Cross-entropy loss

FPN

Feature pyramid network

R-CNN

Region-based convolutional neural network

RoI

Region of interest

RPN

Region proposal network

SSD

Single shot multibox detector

VGG

Very deep convolutional networks

YOLO

You only look once

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Liu, J., Pan, C. & Yan, W.Q. Litter Detection from Digital Images Using Deep Learning. SN COMPUT. SCI. 4, 134 (2023). https://doi.org/10.1007/s42979-022-01568-1

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