A Method of Mining Truck Loading Volume Detection Based on Deep Learning and Image Recognition

Sun, Xiaoyu; Li, Xuerao; Xiao, Dong; Chen, Yu; Wang, Baohua

doi:10.3390/s21020635

Cited by 15 publications

(5 citation statements)

References 16 publications

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“…The loop CNN first receives the image database as a 3D matrix, and after training, selects the closest faces with acceptable accuracy. In the experimental analysis, the comparison between cyclic CNN and traditional CNN shows the effectiveness of the proposed recur-sive CNN [20]. Sharma proposed the convolutional nerve with residual connection to perform the task of automatically identifying tree species from the scan image of wood core [21].…”

Section: Literature Reviewmentioning

confidence: 99%

Slope Shape and Edge Intelligent Recognition Technology Based on Deep Neural Sensing Network

Huang

Yu²,

Wu³

et al. 2022

Journal of Sensors

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In order to solve the problem that the slope surface diseases cannot be accurately identified, which cannot be repaired in time and cause serious slope disasters, a slope intelligent recognition technology based on deep neural network is proposed. Based on convolutional neural network (CNN) theory, the technology adopts the transfer learning method to solve the overfitting problem of slope surface samples, which is difficult to obtain a large number of marked samples, and verifies the proposed model by experiment. The results are as follows: the recognition results of various slope surface diseases by ResNet-18 network are higher than AlexNet and VGG-16, with an average accuracy of 84.1%, and the recognition effect of cracks is the best. Under the same migration strategy, the detection accuracy of ResNet-18 is 96.3%, which is much higher than the other two, and the detection time is reduced by 15% on average. It is proved that the ResNet-18 model proposed can identify slope changes very effectively, so that workers can be timely dispatched for maintenance, reducing the possibility of disaster, which has great significance.

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Section: Literature Reviewmentioning

confidence: 99%

Slope Shape and Edge Intelligent Recognition Technology Based on Deep Neural Sensing Network

Huang

Yu²,

Wu³

et al. 2022

Journal of Sensors

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“…TensorFlow is the second-generation artificial intelligence learning system developed by Google based on DistBelief ( Lozano Jimenez et al, 2019 ; Sun et al, 2021 ). Its name comes from its operating principle.…”

Section: Methodsmentioning

confidence: 99%

Application of Deep Learning Algorithms to Visual Communication Courses

Wang

et al. 2021

Front. Psychol.

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There are rare studies on the combination of visual communication courses and image style transfer. Nevertheless, such a combination can make students understand the difference in perception brought by image styles more vividly. Therefore, a collaborative application is reported here combining visual communication courses and image style transfer. First, the visual communication courses are sorted out to obtain the relationship between them and image style transfer. Then, a style transfer method based on deep learning is designed, and a fast transfer network is introduced. Moreover, the image rendering is accelerated by separating training and execution. Besides, a fast style conversion network is constructed based on TensorFlow, and a style model is obtained after training. Finally, six types of images are selected from the Google Gallery for the conversion of image style, including landscape images, architectural images, character images, animal images, cartoon images, and hand-painted images. The style transfer method achieves excellent effects on the whole image besides the part hard to be rendered. Furthermore, the increase in iterations of the image style transfer network alleviates lack of image content and image style. The image style transfer method reported here can quickly transmit image style in less than 1 s and realize real-time image style transmission. Besides, this method effectively improves the stylization effect and image quality during the image style conversion. The proposed style transfer system can increase students’ understanding of different artistic styles in visual communication courses, thereby improving the learning efficiency of students.

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“…One of the determining factors for the success of the mining process is the stripping of the overburden [3]. Calculation and analysis of mine progress during overburden stripping activities can be carried out by calculating production volume from progress survey [4] results using Surpac 6.5.1 software, the truck count method [5][6], the prismoid formula.…”

Section: Introductionmentioning

confidence: 99%

Analysis of difference in results of overburden production volume using Surpac 6.5.1 software and truck count method at Pit 2 PT. Baturona Adimulya

Harsiga,

Pebrianto

2023

IOP Conf. Ser.: Earth Environ. Sci.

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The overburden production volume at PT Baturona Adimulya was known from two methods of measurement: joint survey activities to produce survey progress data and truck count methods. The total volume of overburden excavation based on the survey results from January until October was 2.415.231 BCM, while the truck count was 2.522.862 BCM, which was known from the daily report of OB removal data from January to October by the surveying division of PT AWU. There is a difference in results every month for both methods. The biggest difference occurred in June at 27.289 BCM, with the total difference in the calculation of overburden volume from January to October at 107.631 BCM. Observations in the field revealed several factors that caused difference between the calculation of the excavation volume based on the survey and the truck count from January–October. Factors that influence the difference from the number difference of 107.631 BCM are double handling, load variation, type of overburden material, the material is still attached to the vessel (1.2182 m3 per trip on the Isuzu giga fvz, 1.2061 m3 per trip on the Cat D400E ADT, 1.0936 m3 per trip), human error, and errors in the implementation of measurement activities.

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A Method of Mining Truck Loading Volume Detection Based on Deep Learning and Image Recognition

Cited by 15 publications

References 16 publications

Slope Shape and Edge Intelligent Recognition Technology Based on Deep Neural Sensing Network

Slope Shape and Edge Intelligent Recognition Technology Based on Deep Neural Sensing Network

Application of Deep Learning Algorithms to Visual Communication Courses

Analysis of difference in results of overburden production volume using Surpac 6.5.1 software and truck count method at Pit 2 PT. Baturona Adimulya

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