Open Access
ARTICLE
Deep Learning Implemented Visualizing City Cleanliness Level by Garbage Detection
M. S. Vivekanandan1, T. Jesudas2,*
1
Anna University, Chennai, 600025, Tamil Nadu, India
2
Mahendra Engineering College (Autonomous), Namakkal, 637503, Tamil Nadu, India
* Corresponding Author: T. Jesudas. Email:
Intelligent Automation & Soft Computing 2023, 36(2), 1639-1652. https://doi.org/10.32604/iasc.2023.032301
Received 13 May 2022; Accepted 12 July 2022; Issue published 05 January 2023
A correction of this article was approved in:
Correction: Deep Learning Implemented Visualizing City Cleanliness Level by Garbage Detection
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Abstract
In an urban city, the daily challenges of managing cleanliness are the
primary aspect of routine life, which requires a large number of resources, the
manual process of labour, and budget. Street cleaning techniques include street
sweepers going away to different metropolitan areas, manually verifying if the
street required cleaning taking action. This research presents novel street garbage
recognizing robotic navigation techniques by detecting the city’s street-level
images and multi-level segmentation. For the large volume of the process, the
deep learning-based methods can be better to achieve a high level of classification, object detection, and accuracy than other learning algorithms. The proposed
Histogram of Oriented Gradients (HOG) is used to features extracted while using
the deep learning technique to classify the ground-level segmentation process’s
images. In this paper, we use mobile edge computing to process street images
in advance and filter out pictures that meet our needs, which significantly affect
recognition efficiency. To measure the urban streets’ cleanliness, our street cleanliness assessment approach provides a multi-level assessment model across different layers. Besides, with ground-level segmentation using a deep neural network,
a novel navigation strategy is proposed for robotic classification. Single Shot MultiBox Detector (SSD) approaches the output space of bounding boxes into a set of
default boxes over different feature ratios and scales per attribute map location
from the dataset. The SSD can classify and detect the garbage’s accurately and
autonomously by using deep learning for garbage recognition. Experimental
results show that accurate street garbage detection and navigation can reach
approximately the same cleaning effectiveness as traditional methods.
Keywords
Cite This Article
M. S. Vivekanandan and T. Jesudas, "Deep learning implemented visualizing city cleanliness level by garbage detection,"
Intelligent Automation & Soft Computing, vol. 36, no.2, pp. 1639–1652, 2023. https://doi.org/10.32604/iasc.2023.032301