Application of Lightweight Convolutional Neural Network for Damage Detection of Conveyor Belt

Zhang, Mengchao; Zhang, Yuan; Zhou, Manshan; Jiang, Kai; Shi, Hao; Yu, Yan; Hao, Nini

doi:10.3390/app11167282

Cited by 24 publications

(9 citation statements)

References 40 publications

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“…Compared to traditional image processing methods that manually select image features and set classification standards, the features obtained by machine vision methods have stronger generalization and robustness, and are suitable for more complex scenes. The recognition of the solidified layer is based on image object detection, and the YOLO v8 network model is selected as the basic structure of the object detection algorithm [22]. Then, the network structure is fine tuned according to the characteristics of complex images and large targets, ultimately achieving the intelligent recognition function of the solidified layer.…”

Section: Yolo V8 Object Detection Algorithmmentioning

confidence: 99%

Research on Solidification Layer Detection in Coal Transportation Process Based on Improved YOLO v8 Algorithm

Xiang,

Zhou,

Huang

et al. 2024

Preprint

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Railway transportation is one of the main modes of long-distance coal transportation, and it is inevitable to cause environmental pollution during the transportation process. In order to improve the environment along the railway and increase the utilization rate of coal resources, this paper proposes a detection algorithm for scattered coal solidification layer during transportation based on the YOLO v8 model, and designs an intelligent recognition model suitable for coal solidification layer detection devices by combining fluorescence detection methods. Through experimental analysis, the model meets the requirements of practical detection and plays a crucial role in environmental protection, with strong practical application value.

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Section: Yolo V8 Object Detection Algorithmmentioning

confidence: 99%

Research on Solidification Layer Detection in Coal Transportation Process Based on Improved YOLO v8 Algorithm

Xiang,

Zhou,

Huang

et al. 2024

Preprint

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“…Therefore, it cannot be considered a purely general requirement, as it is affected by the choice of metric. Real-time capability is a strictly technical challenge, as it can be defined by the number of frames per second (FPS) a model can process but is mostly specific, as it is mainly required when inspecting manufactured goods on a conveyor belt or rails/streets from a fast-moving vehicle for maintenance [39][40][41][42]. Hardware constraints are the most specific and rare technical challenge found in our publication corpus.…”

Section: Requirements For Deep-learning Models In Avimentioning

confidence: 99%

Deep Learning for Automated Visual Inspection in Manufacturing and Maintenance: A Survey of Open- Access Papers

Hütten,

Alves Gomes,

Hölken

et al. 2024

ASI

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Quality assessment in industrial applications is often carried out through visual inspection, usually performed or supported by human domain experts. However, the manual visual inspection of processes and products is error-prone and expensive. It is therefore not surprising that the automation of visual inspection in manufacturing and maintenance is heavily researched and discussed. The use of artificial intelligence as an approach to visual inspection in industrial applications has been considered for decades. Recent successes, driven by advances in deep learning, present a possible paradigm shift and have the potential to facilitate automated visual inspection, even under complex environmental conditions. For this reason, we explore the question of to what extent deep learning is already being used in the field of automated visual inspection and which potential improvements to the state of the art could be realized utilizing concepts from academic research. By conducting an extensive review of the openly accessible literature, we provide an overview of proposed and in-use deep-learning models presented in recent years. Our survey consists of 196 open-access publications, of which 31.7% are manufacturing use cases and 68.3% are maintenance use cases. Furthermore, the survey also shows that the majority of the models currently in use are based on convolutional neural networks, the current de facto standard for image classification, object recognition, or object segmentation tasks. Nevertheless, we see the emergence of vision transformer models that seem to outperform convolutional neural networks but require more resources, which also opens up new research opportunities for the future. Another finding is that in 97% of the publications, the authors use supervised learning techniques to train their models. However, with the median dataset size consisting of 2500 samples, deep-learning models cannot be trained from scratch, so it would be beneficial to use other training paradigms, such as self-supervised learning. In addition, we identified a gap of approximately three years between approaches from deep-learning-based computer vision being published and their introduction in industrial visual inspection applications. Based on our findings, we additionally discuss potential future developments in the area of automated visual inspection.

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“…Modern computational methods underlying neural networks include discrete event simulation, which has made it possible, based on the sensors' swarm, to coordinate processes in the development of depleted gold deposits with a relatively low metal content and profitability compared to rich gold deposits [78]. Furthermore, deep learning based on lightweight convolutional neural networks allowed for the timely detection of the damage of machines and equipment by analyzing a large number of images (up to 100) every second with a test accuracy of 93.22% when integrating MobileNet and Yolov4 networks [79]. The use of a convolutional neural network (1D CNN) to analyze the causes of breakdowns in drilling equipment made it possible to use artificial intelligence to eliminate the human factor and search for technical and mining sources of breakdowns with an accuracy of 88.7% [80].…”

Section: Review Of End-to-end Technologies Of Industry 40 In Surface ...mentioning

confidence: 99%

Development of Surface Mining 4.0 in Terms of Technological Shock in Energy Transition: A Review

Zhironkin

Taran

2023

Energies

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The expansion of end-to-end Industry 4.0 technologies in various industries has caused a technological shock in the mineral resource sector, wherein itsdigital maturity is lower than in the manufacturing sector. As a result of the shock, the productivity and profitability of raw materials extraction has begun to lag behind the industries of its deep processing, which, in the conditions of volatile raw materials markets, can provoke sectoral crises. The diffusion of Industry 4.0 technologies in the mining sector (Mining 4.0) can prevent a technological shock if they are implemented in all segments, including quarrying (Surface Mining 4.0). The Surface Mining 4.0 technological platform would connect the advanced achievements of the Fourth Industrial Revolution (end-to-end digital artificial intelligence technologies, cyber-physical systems and unmanned production with traditional geotechnology) without canceling them, but instead bringing them to a new level of productivity, resource consumption, and environmental friendliness. In the future, the development of Surface Mining 4.0 will provide a response to the technological shock associated with the acceleration of the digital modernization of the mining sector and the increase in labor productivity, which are reducing the operating costs of raw materials extraction. In this regard, the given review is an attempt to analyze the surface mining digital transformation over the course of the diffusion of Industry 4.0 technologies covered in scientific publications. The authors tried to show the core and frontiers of Surface Mining 4.0 development to determine the production, economic, and social effect of replacing humans with digital and cyber-physical systems in the processes of mineral extraction. Particular attention was paid to the review of research on the role of Surface Mining 4.0 in achieving sustainable development goals.

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Application of Lightweight Convolutional Neural Network for Damage Detection of Conveyor Belt

Cited by 24 publications

References 40 publications

Research on Solidification Layer Detection in Coal Transportation Process Based on Improved YOLO v8 Algorithm

Research on Solidification Layer Detection in Coal Transportation Process Based on Improved YOLO v8 Algorithm

Deep Learning for Automated Visual Inspection in Manufacturing and Maintenance: A Survey of Open- Access Papers

Development of Surface Mining 4.0 in Terms of Technological Shock in Energy Transition: A Review

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