3D Reconstruction of Oil Refinery Buildings Using a Depth Camera

Li, Shuaihao; He, Yanxiang; Yang, Xi; Li, Qianqian; Chen, Min

doi:10.1007/s10553-018-0967-y

Cited by 4 publications

(1 citation statement)

References 14 publications

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“…First, feature map mapping is conducted. At this point, 1 × 1 convolution of three unshared weights is used to obtain the feature graph expressed as F Query , F Key , and F value , and then the self-attention graph is obtained through the first two feature graphs, and the specific formula of attention value is as follows [ 21 – 23 ]:

and then the feature graph F org att with the mask is further obtained, and now it has to take the product of β i , j and F value , and the specific formula is as follows [ 24 ]:

…”

Section: Cultural Relic Recognition Based On Self-attention Concentrationmentioning

confidence: 99%

Museum Relic Image Detection and Recognition Based on Deep Learning

Wang

2022

Computational Intelligence and Neuroscience

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To improve the accuracy of museum cultural relic image recognition, the DenseNet and ResNet are selected as the backbone neural networks for detection and recognition. In view of the small target problem in cultural relics, the feature pyramid is introduced in this paper to improve the DenseNet method. The accuracy of target detection is improved through multiscale feature extraction and fusion. At the same time, aiming the problem of weak robustness and feature extraction of cultural relic images, the attention mechanism is proposed to improve ResNet. Therefore, this network can pay attention to the key of feature areas in the image. Finally, the aforementioned methods are verified by experiments. The results show that compared with the YOLOv3 and other algorithms, the accuracy of the improved ResNet proposed in this experiment is above 90%. Furthermore, the number of missed and erroneous detection is the lowest, which are 171 and 134, respectively. The identified mAP indicator accuracy can reach 86%, which also exceeds SVD-Net and DenseNet. It can be seen that the constructed method can effectively detect and recognize the museum cultural relic images.

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and then the feature graph F org att with the mask is further obtained, and now it has to take the product of β i , j and F value , and the specific formula is as follows [ 24 ]:

…”

Section: Cultural Relic Recognition Based On Self-attention Concentrationmentioning

confidence: 99%