Multi-Modal Imaging-Based Foreign Particle Detection System on Coal Conveyor Belt

Saran, Gaurav; Ganguly, Adity; Tripathi, Vineet Ranjan; Kumar, Anupam; Gigie, Andrew; Bhaumik, Chirabrata; Chakravarty, Tapas

doi:10.1007/s12666-021-02492-3

Cited by 14 publications

(9 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We compared our method with four other object detection methods. For example, convolutional neural network model [ 18 ], RDU-Net model [ 17 ], YoloV3 model and improved LeNet method [ 14 ]. The results are shown in Table 4 .…”

Section: Resultsmentioning

confidence: 99%

“…Xiao et al [ 16 ] suggested using a more objective approach to image segmentation based on the RDU net model, which combines the residual structure of the convolutional neural network with the dunet model. Zhang et al [ 17 ] presented a method for detecting foreign objects in coal using machine vision based on an attention neural network. By utilizing visualization technology, this technique developed a CNN with an attention module to successfully detect foreign objects in coal.…”

Section: Introductionmentioning

confidence: 99%

“…However, the model was too huge, and the detection was too GPU-dependent. A multimodal imaging-based conveyor belt foreign object detection method has been proposed by GURAV Saran [ 18 ] et al It collects images using a polarizing camera and identifies foreign objects by displaying different colours for various materials underneath the camera. However, the system recognition delay is 1 second, and the real-time performance is subpar.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Hazard source detection of longitudinal tearing of conveyor belt based on deep learning

Wang

Miao

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

Belt tearing is the main safety accident of belt conveyor. The main cause of tearing is the doped bolt and steel in the conveying belt. In this paper, the bolt and steel are identified as the Hazard source of tear. In this paper, bolt and steel are defined as the risk sources of tearing. Effective detection of the source of danger can effectively prevent the occurrence of conveyor belt tearing accidents. Here we use deep learning to detect the hazard source image. We improved on the SSD(Single Shot MultiBox Detector) model. Replace the original backbone network with an improved Shufflenet_V2, and replace the original position loss function with the CIoU loss function. Additionally, it compares this new approach to previous methods. The proposed model has surpassed other state-of-art methods with more than 94% accuracy. In addition, when deployed without GPU acceleration, the detection speed can reach 20fps. It can meet the requirements of real-time detection. The experimental results show that the proposed model can realize the online detection of hazard sources, so as to prevent longitudinal tearing of conveyor belt.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Hazard source detection of longitudinal tearing of conveyor belt based on deep learning

Wang

Miao

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…A coal and gangue image recognition method based on a nonlinear greyscale compression expansion co-occurrence matrix was proposed by Le et al [8] The above method uses conventional image recognition technology to detect a piece of foreign object that looks like gangue. Based on the idea that various materials exhibit different colors when viewed through a polarizing camera, Gaurav Saran [9] and colleagues proposed a Multi-Modal imaging foreign object detection method using the secondary image of the polarizing camera. This method helps locate foreign objects moving on a conveyor belt.…”

Section: With the Outstanding Performance Of Machine Visionmentioning

confidence: 99%

Foreign Object Detection Method of Conveyor Belt Based on Improved Nanodet

2023

View full text Add to dashboard Cite

show abstract

“…In the same line, but applied to other industries, in [25], an improved tiny version of YOLOV3 is used to detect coal and gangue on a coal conveyor belt. In [26], meanwhile, background subtraction and segmentation is used to detect foreign materials (metal, wood, rubber, etc. ), and in [27], ResNet, VGG and DenseNet architectures are used to detect imperfections in thermoforming food packages.…”

Section: Related Workmentioning

confidence: 99%

MOSPPA: monitoring system for palletised packaging recognition and tracking

Castaño-Amorós

Fuentes²,

Gil³

2023

Int J Adv Manuf Technol

View full text Add to dashboard Cite

The paper industry manufactures corrugated cardboard packaging, which is unassembled and stacked on pallets to be supplied to its customers. Human operators usually classify these pallets according to the physical features of the cardboard packaging. This process can be slow, causing congestion on the production line. To optimise the logistics of this process, we propose a visual recognition and tracking pipeline that monitors the palletised packaging while it is moving inside the factory on roller conveyors. Our pipeline has a two-stage architecture composed of Convolutional Neural Networks, one for oriented pallet detection and recognition, and another with which to track identified pallets. We carried out an extensive study using different methods for the pallet detection and tracking tasks and discovered that the oriented object detection approach was the most suitable. Our proposal recognises and tracks different configurations and visual appearance of palletised packaging, providing statistical data in real time with which to assist human operators in decision-making. We tested the precision-performance of the system at the Smurfit Kappa facilities. Our proposal attained an Average Precision (AP) of 0.93 at 14 Frames Per Second (FPS), losing only 1% of detections. Our system is, therefore, able to optimise and speed up the process of logistic distribution.

show abstract

Multi-Modal Imaging-Based Foreign Particle Detection System on Coal Conveyor Belt

Cited by 14 publications

References 7 publications

Hazard source detection of longitudinal tearing of conveyor belt based on deep learning

Hazard source detection of longitudinal tearing of conveyor belt based on deep learning

Foreign Object Detection Method of Conveyor Belt Based on Improved Nanodet

MOSPPA: monitoring system for palletised packaging recognition and tracking

Contact Info

Product

Resources

About