Development of a method of detection and classification of waste objects on a conveyor for a robotic sorting system

Seredkin, A. V.; Tokarev, M. P.; Plohih, I. A.; Gobyzov, O. A.; Маркович, Д. М.

doi:10.1088/1742-6596/1359/1/012127

Cited by 26 publications

(10 citation statements)

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“…Automated inspection and monitoring of material on conveyor belts is common in many industries [7, 8] and increasingly important for waste recycling [9, 10] and agrifood [11, 12]. Our work demonstrates that, in these scenarios, RGBD imagery can be acquired from existing monocular cameras.…”

Section: Introductionmentioning

confidence: 91%

Motion stereo at sea: Dense 3D reconstruction from image sequences monitoring conveyor systems on board fishing vessels

Fisher

French

Gorpincenko

et al. 2022

IET Image Processing

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A system that reconstructs 3D models from a single camera monitoring fish transported on a conveyor system is investigated. Models are subsequently used for training a species classifier and for improving estimates of discarded biomass. It is demonstrated that a monocular camera, combined with a conveyor's linear motion produces a constrained form of multiview structure from motion, that allows the 3D scene to be reconstructed using a conventional stereo pipeline analogous to that of a binocular camera. Although motion stereo was proposed several decades ago, the present work is the first to compare the accuracy and precision of monocular and binocular stereo cameras monitoring conveyors and operationally deploy a system. The system exploits Convolutional Neural Networks (CNNs) for foreground segmentation and stereo matching. Results from a laboratory model show that when the camera is mounted 750 mm above the conveyor, a median accuracy of <5 mm can be achieved with an equivalent baseline of 62 mm. The precision is largely limited by error in determining the equivalent baseline (i.e. distance travelled by the conveyor belt). When ArUco markers are placed on the belt, the inter quartile range (IQR) of error in z (depth) near the optical centre was found to be ±4 mm.

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Section: Introductionmentioning

confidence: 91%

Motion stereo at sea: Dense 3D reconstruction from image sequences monitoring conveyor systems on board fishing vessels

Fisher

French

Gorpincenko

et al. 2022

IET Image Processing

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“…Deep learning was applied to classify plastic, glass, organic waste and paper, where 70% accuracy was achieved by five-layer architecture and 61.67% accuracy was achieved by four-layer architecture. Seredkin et al [22] trained a model on approximately 13,000 solid waste images and achieved an accuracy of 64%. The solid waste was shifted onto the conveyer belt and the CNN-based model sorted the solid waste into three classified categories: metal can, polythene and plastic bottle.…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning-Based Intelligent Garbage Detection System Using an Unmanned Aerial Vehicle

Verma

Gupta

et al. 2022

Symmetry

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A population explosion has resulted in garbage generation on a large scale. The process of proper and automatic garbage collection is a challenging and tedious task for developing countries. This paper proposes a deep learning-based intelligent garbage detection system using an Unmanned Aerial Vehicle (UAV). The main aim of this paper is to provide a low-cost, accurate and easy-to-use solution for handling the garbage effectively. It also helps municipal corporations to detect the garbage areas in remote locations automatically. This automation was derived using two Convolutional Neural Network (CNN) models and images of solid waste were captured by the drone. Both models were trained on the collected image dataset at different learning rates, optimizers and epochs. This research uses symmetry during the sampling of garbage images. Homogeneity regarding resizing of images is generated due to the application of symmetry to extract their characteristics. The performance of two CNN models was evaluated with the state-of-the-art models using different performance evaluation metrics such as precision, recall, F1-score, and accuracy. The CNN1 model achieved better performance for automatic solid waste detection with 94% accuracy.

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“…Zhang et al [22] used the Faster RCNN algorithm to detect 681 street pictures with 9 categories of garbage targets, and the detection mAP was 0.82, but there was a problem of unbalanced categories. Seredkin et al [23] used Faster RCNN network to perform garbage classification which has high accuracy and effectively realized garbage identification. Chen et al [24] used the Faster RCNN algorithm to detect 199 garbage targets on the pipeline and obtained a system missed identification rate of 3% and a false identification rate of 9%.…”

Section: Related Workmentioning

confidence: 99%

A Garbage Detection and Classification Method Based on Visual Scene Understanding in the Home Environment

Shen

Liu

et al. 2021

Complexity

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Garbage classification is a social issue related to people’s livelihood and sustainable development, so letting service robots autonomously perform intelligent garbage classification has important research significance. Aiming at the problems of complex systems with data source and cloud service center data transmission delay and untimely response, at the same time, in order to realize the perception, storage, and analysis of massive multisource heterogeneous data, a garbage detection and classification method based on visual scene understanding is proposed. This method uses knowledge graphs to store and model items in the scene in the form of images, videos, texts, and other multimodal forms. The ESA attention mechanism is added to the backbone network part of the YOLOv5 network, aiming to improve the feature extraction ability of the network, combining with the built multimodal knowledge graph to form the YOLOv5-Attention-KG model, and deploying it to the service robot to perform real-time perception on the items in the scene. Finally, collaborative training is carried out on the cloud server side and deployed to the edge device side to reason and analyze the data in real time. The test results show that, compared with the original YOLOv5 model, the detection and classification accuracy of the proposed model is higher, and the real-time performance can also meet the actual use requirements. The model proposed in this paper can realize the intelligent decision-making of garbage classification for big data in the scene in a complex system and has certain conditions for promotion and landing.

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Development of a method of detection and classification of waste objects on a conveyor for a robotic sorting system

Cited by 26 publications

References 1 publication

Motion stereo at sea: Dense 3D reconstruction from image sequences monitoring conveyor systems on board fishing vessels

Motion stereo at sea: Dense 3D reconstruction from image sequences monitoring conveyor systems on board fishing vessels

A Deep Learning-Based Intelligent Garbage Detection System Using an Unmanned Aerial Vehicle

A Garbage Detection and Classification Method Based on Visual Scene Understanding in the Home Environment

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