CAP-YOLO: Channel Attention Based Pruning YOLO for Coal Mine Real-Time Intelligent Monitoring

Xu, Zhi; Li, Jingzhao; Meng, Yifan; Zhang, Xiaoming

doi:10.3390/s22124331

Cited by 19 publications

(9 citation statements)

References 48 publications

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“…To achieve real-time intelligent analysis for coal mine surveillance videos, the Channel-Attention-Based Pruning YOLO and Adaptive Image Enhancement Parameter To achieve real-time intelligent analysis for coal mine surveillance videos, the Channel-Attention-Based Pruning YOLO and Adaptive Image Enhancement Parameter Selection Module were proposed [87]. Another advanced intelligent monitoring system comprises a video acquiring unit, a working face dip angle detection unit, and a coal seam geological detecting instrument [88].…”

Section: Mining Machines Intelligent Monitoringmentioning

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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Section: Mining Machines Intelligent Monitoringmentioning

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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“…Although this approach's detection accuracy is good, its speed is slow. Therefore, regression-based one-stage detectors represented by the You Only Look Once (YOLO) series [16][17][18][19][20][21][22][23] and single shot multi-box detector (SSD) [24], known for their high detection accuracy and speed, were used to identify coal mine miners' unsafe behaviors. In order to identify whether security equipment is being worn, the SSD model was improved by replacing the feature extraction network visual geometry group (VGG- 16) with MobileNet [25].…”

Section: Literature Surveymentioning

confidence: 99%

Study on the Recognition of Coal Miners’ Unsafe Behavior and Status in the Hoist Cage Based on Machine Vision

Yao,

Wang,

Nie

et al. 2023

Sensors

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The hoist cage is used to lift miners in a coal mine’s auxiliary shaft. Monitoring miners’ unsafe behaviors and their status in the hoist cage is crucial to production safety in coal mines. In this study, a visual detection model is proposed to estimate the number and categories of miners, and to identify whether the miners are wearing helmets and whether they have fallen in the hoist cage. A dataset with eight categories of miners’ statuses in hoist cages was developed for training and validating the model. Using the dataset, the classical models were trained for comparison, from which the YOLOv5s model was selected to be the basic model. Due to small-sized targets, poor lighting conditions, and coal dust and shelter, the detection accuracy of the Yolov5s model was only 89.2%. To obtain better detection accuracy, k-means++ clustering algorithm, a BiFPN-based feature fusion network, the convolutional block attention module (CBAM), and a CIoU loss function were proposed to improve the YOLOv5s model, and an attentional multi-scale cascaded feature fusion-based YOLOv5s model (AMCFF-YOLOv5s) was subsequently developed. The training results on the self-built dataset indicate that its detection accuracy increased to 97.6%. Moreover, the AMCFF-YOLOv5s model was proven to be robust to noise and light.

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“…They have been widely used in various fields, bringing convenience to people's lives. For example, the YOLO series have been applied to intelligent monitoring of video content [23], quality assurance of crops [24], various products, etc. Among them, the defect detection of objects has brought great changes to quality detection, making intelligent detection replace the traditional manual detection.…”

Section: Related Workmentioning

confidence: 99%

The RFID Electronic Tag Defect Detection System Based On YOLOv5 For Industrial Scenarios

Zhou¹,

Ding²,

Du³

et al. 2022

Proceedings of International Exchange and Innovation Conference on Engineering &Amp; Sciences (IEICES)

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Recent technological progress has promoted a large number of development trends in the Radio Frequency Identification (RFID) system. RFID tag detection/identification is one of the most critical problems in successfully deploying the RFID system in various applications. However, many significant issues related to detecting RFID tag defects have not been addressed. The main problems of RFID electronic tag defect detection methods in industrial scenarios are low efficiency, low accuracy, and high labor costs. This paper analyses the characteristics of RFID tag defects based on defect samples, and a detection and prediction method related to YOLOv5 is proposed. In this experiment, a real-time RFID tag defect intelligent detection system embedded in the YOLOv5 model was developed and applied to industrial production. The results show that this system has higher accuracy in defect detection, faster detection time, and saves labor costs, which can meet industrial needs.

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CAP-YOLO: Channel Attention Based Pruning YOLO for Coal Mine Real-Time Intelligent Monitoring

Cited by 19 publications

References 48 publications

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

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

Study on the Recognition of Coal Miners’ Unsafe Behavior and Status in the Hoist Cage Based on Machine Vision

The RFID Electronic Tag Defect Detection System Based On YOLOv5 For Industrial Scenarios

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