YOLOv5-MHSA-DS: an efficient pig detection and counting method

Author:

Hao Wangli1,Zhang Li1,Xu Shu-ai1,Han Meng1,Li Fuzhong1,Yang Hua1

Affiliation:

1. Shanxi Agricultural University, Jinzhong, Shanxi, People's Republic of China

Funder

Shanxi Province Basic Research Program

Shanxi Agricultural University Science and Technology Innovation Enhancement Project

Shanxi Province Higher Education Teaching Reform and Innovation Project

Shanxi Postgraduate Education and Teaching Reform Project Fund

Shanxi Agricultural University doctoral research start-up project

Shanxi Agricultural University Academic Restoration Research Project

Publisher

Informa UK Limited

Reference28 articles.

1. Bochkovskiy A. Wang C.-Y. & Liao H.-Y. M. (2020). YOLOv4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934.

2. Bodla N. Singh B. Chellappa R. & Davis L. (2017). Improving object detection with one line of code. arXiv preprint arXiv:1704.04503.

3. R-FCN: Object detection via region-based fully convolutional networks;Dai J.;Advances in Neural Information Processing Systems,2016

4. Towards real-time object detection with region proposal networks;Faster R.;Advances in Neural Information Processing Systems,2015

5. YOLOv5-SA-FC: A Novel Pig Detection and Counting Method Based on Shuffle Attention and Focal Complete Intersection over Union

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