Herd counting based on VDNet convolutional neural network

DU Yongxing; MIAO Xiaowei; QIN Ling; LI Baoshan

doi:10.7510/jgjs.issn.1001-3806.2021.05.023

LASER TECHNOLOGY > 2021 > 45(5): 675-680. > DOI: 10.7510/jgjs.issn.1001-3806.2021.05.023

DU Yongxing, MIAO Xiaowei, QIN Ling, LI Baoshan. Herd counting based on VDNet convolutional neural network[J]. LASER TECHNOLOGY, 2021, 45(5): 675-680. DOI: 10.7510/jgjs.issn.1001-3806.2021.05.023

Citation:

DU Yongxing, MIAO Xiaowei, QIN Ling, LI Baoshan. Herd counting based on VDNet convolutional neural network[J]. LASER TECHNOLOGY, 2021, 45(5): 675-680. DOI: 10.7510/jgjs.issn.1001-3806.2021.05.023

Citation:

DU Yongxing, MIAO Xiaowei, QIN Ling, LI Baoshan. Herd counting based on VDNet convolutional neural network[J]. LASER TECHNOLOGY, 2021, 45(5): 675-680. DOI: 10.7510/jgjs.issn.1001-3806.2021.05.023

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Herd counting based on VDNet convolutional neural network

School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou 014000, China

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Received Date: September 08, 2020
Revised Date: October 08, 2020
Published Date: September 24, 2021

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Abstract

Abstract

In order to avoid the interference of mutual occlusion between sheep in the traditional flock counting task and improve the accuracy of flock counting, the VDNet(VGG-16+DC net) convolutional neural network flock counting method, combining visual geometry group(VGG) 16 and dialated convolution (DC) net, was adopted. VGG-16 with the fully connected layer removed was used at the front end of the network to extract 2-D features, 6 layers of DC with different dilated rates was used to extract more advanced features. DC expanded the receptive field, replaced the pooling operation, and decreased the complexity of the network while kept the resolution unchanged at the same time. The theoretical analysis and experimental verification were carried out. Finally, a convolutional layer with a convolution kernel size of 1×1 was used to output a high-quality density map, and then the number of sheep in the input image was obtained by integrating the pixels of the density map. The results show that the average absolute error of the counting method in this paper is 2.51, the mean square error is 3.74, and the average accuracy is 93%, respectively. This result is helpful for the task of counting sheep.
- image processing,
- sheep count,
- dilated convolution,
- convolutional neural network

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References (21)

References

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