Autonomous garbage detection for intelligent urban management

Wang, Ying; Zhang, Xu

doi:10.1051/matecconf/201823201056

Cited by 68 publications

(21 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ying Liu et al [10] proposed an improved YOLOv2 model, where the authors tweaked the parameters and used optimization and acceleration algorithms to strike a balance between real-time performance application and precision of target box clustering. Ying Wang et al [6] proposes an autonomous system of garbage detection using the Faster R-CNN opensource framework. Here instead of VGG as the essential convolutional layers, ResNet Network was used.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A deep learning approach based hardware solution to categorise garbage in environment

Gupta

Joshi

Mukhopadhyay

et al. 2021

Complex Intell. Syst.

View full text Add to dashboard Cite

Garbage detection and disposal have become one of the major hassles in urban planning. Due to population influx in urban areas, the rate of garbage generation has increased exponentially along with garbage diversity. In this paper, we propose a hardware solution for garbage segregation at the base level based on deep learning architecture. The proposed deep-learning-based hardware solution SmartBin can segregate the garbage into biodegradable and non-biodegradable using Image classification through a Convolutional Neural Network System Architecture using a Real-time embedded system. Garbage detection via image classification aims for quick and efficient categorization of garbage present in the bin. However, this is an arduous task as garbage can be of any dimension, object, color, texture, unlike object detection of a particular entity where images of objects of that entity do share some similar characteristics and traits. The objective of this work is to compare the performance of various pre-trained Convolution Neural Network namely AlexNet, ResNet, VGG-16, and InceptionNet for garbage classification and test their working along with hardware components (PiCam, raspberry pi, infrared sensors, etc.) used for garbage detection in the bin. The InceptionNet Neural Network showed the best performance measure for the proposed model with an accuracy of 98.15% and a loss of 0.10 for the training set while it was 96.23% and 0.13 for the validation set.

show abstract

Section: Related Workmentioning

confidence: 99%

“…Autonomous Garbage Detection is one such new technique [6] coupled with IoT [7]. In this technique, integration with other data systems such as Geographic Information Systems [8] can provide a system where overflow, as well as pooling of garbage, could be detected without the requirement of explicit human intervention.…”

Section: Introductionmentioning

confidence: 99%

A deep learning approach based hardware solution to categorise garbage in environment

Gupta

Joshi

Mukhopadhyay

et al. 2021

Complex Intell. Syst.

View full text Add to dashboard Cite

show abstract

“…Rad et al [ 32 ] presented an approach similar to OverFeat [ 33 ] for litter object detection. Another similar approach based on Faster RCNN [ 16 ] is presented by Wang and Zhang [ 34 ]. Contrary to the above works, we address the waste object segmentation problem that requires accurate delineation of object boundaries.…”

Section: Related Workmentioning

confidence: 99%

A Multi-Level Approach to Waste Object Segmentation

Wang

Cai

Liang

et al. 2020

Sensors

View full text Add to dashboard Cite

We address the problem of localizing waste objects from a color image and an optional depth image, which is a key perception component for robotic interaction with such objects. Specifically, our method integrates the intensity and depth information at multiple levels of spatial granularity. Firstly, a scene-level deep network produces an initial coarse segmentation, based on which we select a few potential object regions to zoom in and perform fine segmentation. The results of the above steps are further integrated into a densely connected conditional random field that learns to respect the appearance, depth, and spatial affinities with pixel-level accuracy. In addition, we create a new RGBD waste object segmentation dataset, MJU-Waste, that is made public to facilitate future research in this area. The efficacy of our method is validated on both MJU-Waste and the Trash Annotation in Context (TACO) dataset.

show abstract

“…Similarly, the paper [17] makes use of Fast-RCNNs [18] to detect big clusters of garbage. Their dataset seems to be composed of outdoors pictures like the ones that could be taken from the Internet-i.e., it does not depict a real-world street scenario, with pictures taken from a running vehicle.…”

Section: Outdoor Trash Detectionmentioning

confidence: 99%

An Energy Saving Road Sweeper Using Deep Vision for Garbage Detection

Donati,

Fontanini,

Tagliaferri

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

Road sweepers are ubiquitous machines that help preserve our cities cleanliness and health by collecting road garbage and sweeping out dirt from our streets and sidewalks. They are often very mechanical instruments, needing to operate in harsh conditions dealing with all sorts of abandoned trash and natural garbage. They are usually composed of rotating brushes, collector belts and bins, and sometimes water or air streams. All of these mechanical tools are usually high in power demand and strongly subject to wear and tear. Moreover, due to the simple working logic often implied by these cleaning machines, these tools work in an “always on”/“max power” state, and any further regulation is left to the pilot. Therefore, adding artificial intelligence able to correctly operate these tools in a semi-automatic way would be greatly beneficial. In this paper, we propose an automatic road garbage detection system, able to locate with great precision most types of road waste, and to correctly instruct a road sweeper in order to handle them. With this simple addition to an existing sweeper, we will be able to save more than 80% electrical power currently absorbed by the cleaning systems and reduce by the same amount brush weariness (prolonging their lifetime). This is done by choosing when to use the brushes and when not to, with how much strength, and where. The only hardware components needed by the system will be a camera and a PC board able to read the camera output (and communicate via CanBus). The software of the system will be mainly composed of a deep neural network for semantic segmentation of images, and a real-time software program to control the sweeper actuators with the appropriate timings. To prove the claimed results, we run extensive tests onboard of such a truck, as well as benchmark tests for accuracy, sensitivity, specificity and inference speed of the system.

show abstract

Autonomous garbage detection for intelligent urban management

Cited by 68 publications

References 12 publications

A deep learning approach based hardware solution to categorise garbage in environment

A deep learning approach based hardware solution to categorise garbage in environment

A Multi-Level Approach to Waste Object Segmentation

An Energy Saving Road Sweeper Using Deep Vision for Garbage Detection

Contact Info

Product

Resources

About