Affiliation:
1. College of Information Science and Engineering, Shanxi Agricultural University, Taigu / China
Abstract
Individual pig identification technology is the precondition of precise breeding. Taking pig face as the study point, this article puts forward a pig face identification method based on improved AlexNet model and explores the influence of training batch size on the performance of the model. Spatial attention module (SAM) is introduced in AlexNet model to compare the performance of the AlexNet model and the improved model on the training set and the validation set. The study shows that the improved AlexNet model can achieve higher precision rate under different training batch sizes and has higher convergence rate and robustness, with an identification precision rate reaching 98.11%, and a recall rate and f1 value reaching 98.03% and 98.05%. When the training batch sizes are 16, 32, and 64 respectively, the test time of the model, which represents its operating efficiency, improves by 1.99%, 2.36% and 10.31%, respectively, showing better performance in pig face identification. The test results show that different batch sizes have a certain influence on the prediction results of the model, while no fixed relationship.
Publisher
R and D National Institute for Agricultural and Food Industry Machinery - INMA Bucharest
Subject
Industrial and Manufacturing Engineering,Mechanical Engineering,Food Science
Reference19 articles.
1. Chen C., Zhu W.X., Cao J., Ma C.H., Huang W.J., Ruan C.Z., (2017), Image motion feature extraction for recognition of aggressive behaviours among group-housed pigs. Computers and Electronics in Agriculture, Vol.142, pp.380-387, Oxford/England;
2. Chen Y.P., Li J.A., Xiao H.X., Jin X.J., Yan S.C., Feng J.S., (2017), Dual path networks. Advances in Neural Information Processing Systems, pp.4470-4478, New York/USA;
3. Chollet F., (2017), Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1251-1258, Hawaii/USA;
4. Guo Y.Z., Zhu W.X., Jiao P.P., Ma C.H., Yang J.J., (2015), Multi-object extraction from topview group-housed pig images based on adaptive partitioning and multilevel thresholding segmentation. Biosystems Engineering, Vol.135, pp.54-60, San Diego/USA;
5. Hahnel D, Burgard W, Fox D, Fishkin K., Philipose M., (2004), Mapping and localization with RFID technology. IEEE International Conference on Robotics and Automation, pp. 1015-1020, New Orleans, LA/USA;
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