Abstract：To solve the problems of currently traditional detection methods for cow face detection with poor detection effect and easy damage of detection equipment, according to the principles of big data and diversity, the mobile phones and cameras were used to build the dataset with more than 10 000 cows under different conditions of appearance variation, occlusion and illumination change. Using the dataset, the object detection methods based on deep network models of SSD, Faster RCNN and RFCN were improved and compared on the detection performance. The results show that the improved Faster RCNN can achieve the detection accuracy of 0.990 with detection speed of 11 F·s-1. The detection speed of the improved SSD is 47 F·s-1, and the detection accuracy is 0.945, which is slightly lower than that of Faster RCNN.
姚礼垚, 熊浩, 钟依健, 刘财兴, 刘汉兴, 高月芳. 基于深度网络模型的牛脸检测算法比较[J]. 江苏大学学报（自然科学版）, 2019, 40(2): 197-202.
YAO Li-Yao, XIONG Hao, ZHONG Yi-Jian, LIU Cai-Xing, LIU Han-Xing, GAO Yue-Fang. Comparison of cow face detection algorithms based on deep network model[J]. Journal of Jiangsu University(Natural Science Eidtion)
, 2019, 40(2): 197-202.
2019, Vol. 40 
