| dc.description.abstract | Dairy cows are high-economic animals, and their growth and breeding are directly related to identification. Therefore we proposes an identification system for dairy cows in pasture, which can meet the needs of identify by photos taken by remote camera and can add or delete categories easily. Using object detection to find cows in the image, then use image contrast enhancement algorithm to enhance the image and extract the three modal features , including Auto Encoder, horizontal and vertical projection, and assign each cow its own classifier. Adding or reducing categories only needs to add or delete the classifier. Each classifier will extract the three model features separately, and then use the probability neural network to fusion these features, and finally the output of all classifiers are integrate and infer. The experimental results show that our system obtains 93.5% and 88.6% accuracy in fewer training data (10, 5), which
is better than ResNet50′s 86.5% and 70.0%. In the case of 15 training data, we got 94% accuracy slightly lost to 96.0% of ResNet50. In addition, adding a new category of 10 training samples, our system reduces accuracy by 0.9% on average and ResNet50 is 1.23%. This shows that our
system has the advantage of fewer of training data, and not only doesn’t need to retrain the whole network when adding new categories, but also has less impact on accuracy. Shows that it can cope with applications that need to add or delete categories very often. | en_US |