Livestock Detection Based on Convolutional Neural Network
Authors: 
Chao Zuo, Liang Han, Pin Tao, Xiang lei MengAuthors Info & Claims
Chao Zuo, Liang Han, Pin Tao, Xiang lei MengAuthors Info & ClaimsPages 1 - 6
Abstract
In order to keep the ecological balance of grassland and get the number of grazing livestock accurately, an improved livestock detection algorithm based on full convolutional neural network is proposed. First, MultiResUNet is proposed for image segmentation, and morphological operation is performed on the segmented image patches to get candidate regions. Then, the Inception V4 network in conjunction with SENet for strengthening object feature and reducing noise is proposed to classify the candidate regions. Finally, a comparison experiment is made between the proposed method and other livestock detection algorithm. Experimental results show the average accuracy (AP) of the proposed method is superior to the other livestock detection algorithm about 4.2%.
References
[1]
Yandong L I, Zongbo H, Hang L. Survey of convolutional neural network [J]. Journal of Computer Applications, 2016, 36(09):2508-2515+2565.
[2]
Ren S, He K, Girshick R, Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6):1137-1149.
[3]
He K, Gkioxari G, Dollar P, Mask R-CNN[C]. International conference on computer vision, 2017: 2980-2988.
[4]
Redmon J, Farhadi A. YOLOv3: An Incremental Improvement [J]. arXiv: Computer Vision and Pattern Recognition, 2018.
[5]
Liu W, Anguelov D, Erhan D, SSD: Single Shot MultiBox Detector[C]. European conference on computer vision, 2016: 21-37.
[6]
Lin T, Dollar P, Girshick R, Feature Pyramid Networks for Object Detection[C]. Computer Vision and Pattern Recognition, 2017: 936-944.
[7]
Singh B, Davis L S. An Analysis of Scale Invariance in Object Detection - SNIP[C]. Computer Vision and Pattern Recognition, 2018: 3578-3587.
[8]
Xie X, Cao G, Yang W, Feature-fused SSD: fast detection for small objects[C]// Ninth International Conference on Graphic and Image Processing. 2018.
[9]
Cai Z, Vasconcelos N. Cascade R-CNN: Delving Into High Quality Object Detection[C]. Computer Vision and Pattern Recognition, 2018: 6154-6162.
[10]
Han L, Tao P, Martin R R. Livestock detection in aerial images using a fully convolutional network [J]. Computing visual media (English version), 2019, 005(002):221-228.
[11]
Ibtehaz N, Rahman M S. MultiResUNet: Rethinking the U-Net architecture for multimodal biomedical image segmentation [J]. Neural Networks, 2020: 74-87.
[12]
Ronneberger O, Fischer P, Brox T, U-Net: Convolutional Networks for Biomedical Image Segmentation[C]. Medical image computing and computer assisted intervention, 2015: 234-241.
[13]
Szegedy C, Ioffe S, Vanhoucke V, Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning[C]. National conference on artificial intelligence, 2016: 4278-4284.
[14]
Hu J, Shen L, Sun G, Squeeze-and-Excitation Networks[C]. Computer Vision and Pattern Recognition, 2018: 7132-7141.
[15]
Xia G, Bai X, Ding J, DOTA: A Large-Scale Dataset for Object Detection in Aerial Images[C]. Computer Vision and Pattern Recognition, 2018: 3974-3983.
[16]
Cheng G, Zhou P, Han J. Learning Rotation-Invariant Convolutional Neural Networks for Object Detection in VHR Optical Remote Sensing Images [J]. IEEE Transactions on Geoscience & Remote Sensing, 2016, 54(12):7405-7415.
[17]
Redmon J, Divvala S K, Girshick R, You Only Look Once: Unified, Real-Time Object Detection[C]. Computer Vision and Pattern Recognition, 2016: 779-788.
[18]
Redmon J, Farhadi A. YOLO9000: Better, Faster, Stronger[C]. Computer Vision and Pattern Recognition, 2017: 6517-6525.
[19]
Yang X, Fu K, Sun H, R2CNN++: Multi-Dimensional Attention Based Rotation Invariant Detector with Robust Anchor Strategy [J]. arXiv: Computer Vision and Pattern Recognition, 2018.
[20]
Long J, Shelhamer E, Darrell T. Fully Convolutional Networks for Semantic Segmentation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 39(4):640-651.
[21]
Szegedy C, Liu W, Jia Y, Going deeper with convolutions[C]. Computer Vision and Pattern Recognition, 2015: 1-9.
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Published In
August 2020
114 pages
ISBN:9781450388023
DOI:10.1145/3425577
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Published: 23 January 2021
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ICCCV'20
ICCCV'20: 2020 the 3rd International Conference on Control and Computer Vision
August 23 - 25, 2020
Macau, China
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