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Number of results: 4
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Abstract

In order to achieve accurate identification and segmentation of ore under complex working conditions, machine vision and neural network technology are used to carry out intelligent detection research on ore, an improved Mask RCNN instance segmentation algorithm is proposed. Aiming at the problem of misidentification of stacked ores caused by the loss of deep feature details during the feature extraction process of ore images, an improved Multipath Feature Pyramid Network (MFPN) was proposed. The network firstly adds a single bottom-up feature fusion path, and then adds with the top-down feature fusion path of the original algorithm, which can enrich the deep feature details and strengthen the fusion of the network to the feature layer, and improve the accuracy of the network to the ore recognition. The experimental results show that the algorithm proposed in this paper has a recognition accuracy of 96.5% for ore under complex working conditions, and the recall rate and recall rate function values reach 97.4% and 97.0% respectively, and the AP75 value is 6.84% higher than the original algorithm. The detection results of the ore in the actual scene show that the mask size segmented by the network is close to the actual size of the ore, indicating that the improved network model proposed in this paper has achieved a good performance in the detection of ore under different illumination, pose and background. Therefore, the method proposed in this paper has a good application prospect for stacked ore identification under complex working conditions.
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Authors and Affiliations

Hehui Zhou
1
ORCID: ORCID
Gaipin Cai
1 2
Shun Liu
1

  1. School of Mechanical and Electrical Engineering, Jiangxi University of Science and Technology, China
  2. Jiangxi Province Engineering Research Center for Mechanical and Electrical of Mining and Metallurgy, China
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Abstract

Convolutional neural networks have achieved tremendous success in the areas of image processing and computer vision. However, they experience problems with low-frequency information such as semantic and category content and background color, and high-frequency information such as edge and structure. We propose an efficient and accurate deep learning framework called the multi-frequency feature extraction and fusion network (MFFNet) to perform image processing tasks such as deblurring. MFFNet is aided by edge and attention modules to restore high-frequency information and overcomes the multiscale parameter problem and the low-efficiency issue of recurrent architectures. It handles information from multiple paths and extracts features such as edges, colors, positions, and differences. Then, edge detectors and attention modules are aggregated into units to refine and learn knowledge, and efficient multi-learning features are fused into a final perception result. Experimental results indicate that the proposed framework achieves state-of-the-art deblurring performance on benchmark datasets.
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Authors and Affiliations

Jinsheng Deng
1
Zhichao Zhang
2
Xiaoqing Yin
1

  1. College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha 410000, China
  2. College of Computer, National University of Defense Technology, Changsha 410000, China
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Abstract

In detecting cluster targets in ports or near-shore waters, the echo amplitude is seriously disturbed by interface reverberation, which leads to the distortion of the traditional target intensity characteristics, and the appearance of multiple targets in the same or adjacent beam leads to fuzzy feature recognition. Studying and extracting spatial distribution scale and motion features that reflect the information on cluster targets physics can improve the representation accuracy of cluster target characteristics. Based on the highlight model of target acoustic scattering, the target azimuth tendency is accurately estimated by the splitting beam method to fit the spatial geometric scale formed by multiple highlights. The instantaneous frequencies of highlights are extracted from the time-frequency domain, the Doppler shift of the highlights is calculated, and the motion state of the highlights is estimated. Based on the above processing method, target highlights’ orientation, spatial scale and motion characteristics are fused, and the multiple moving highlights of typical formation distribution in the same beam are accurately identified. The features are applied to processing acoustic scattering data of multiple moving unmanned underwater vehicles (UUVs) on a lake. The results show that multiple small moving underwater targets can be effectively recognized according to the highlight scattering characteristics.
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Authors and Affiliations

Yang Yang
1
ORCID: ORCID
Jun Fan
1
Bin Wang
1
ORCID: ORCID

  1. Key Laboratory of Marine Intelligent Equipment and System of the Ministry of Education, Shanghai Jiao Tong University, Shanghai, China
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Abstract

Object tracking based on Siamese networks has achieved great success in recent years, but increasingly advanced trackers are also becoming cumbersome, which will severely limit deployment on resource-constrained devices. To solve the above problems, we designed a network with the same or higher tracking performance as other lightweight models based on the SiamFC lightweight tracking model. At the same time, for the problems that the SiamFC tracking network is poor in processing similar semantic information, deformation, illumination change, and scale change, we propose a global attention module and different scale training and testing strategies to solve them. To verify the effectiveness of the proposed algorithm, this paper has done comparative experiments on the ILSVRC, OTB100, VOT2018 datasets. The experimental results show that the method proposed in this paper can significantly improve the performance of the benchmark algorithm.
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Authors and Affiliations

Zhentao Wang
1
Xiaowei He
1
Rao Cheng
1

  1. College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua, Zhejiang, 321000, China

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