摘要
The increasing consumption of ducks and chickens in China demands characterizing carcasses of domestic birds efficiently. Most existing methods, however, were developed for characterizing carcasses of pigs or cat-tle. Here, we developed a noncontact and automated weighing method for duck carcasses hanging on a produc-tion line. A 2D camera with its facilitating parts recorded the moving duck carcasses on the production line. To esti-mate the weight of carcasses, the images in the acquired dataset were modeled by a convolution neuron network (CNN). This model was trained and evaluated using 10 -fold cross-validation. The model estimated the weight of duck carcasses precisely with a mean abstract deviation (MAD) of 58.8 grams and a mean relative error (MRE) of 2.15% in the testing dataset. Compared with 2 widely used methods, pixel area linear regression and the artificial neural network (ANN) model, our model decreases the estimation error MAD by 64.7 grams (52.4%) and 48.2 grams (45.0%). We release the dataset and code at https://github.com/RuoyuChen10/Image_weighing.
-
单位中国科学院; 东北大学