Real-time behavior detection and judgment of egg breeders based on YOLO v3

Abstract

In order to detect the behavior of egg breeders in self-breeding cages rapidly, a method of target location and behavior recognition based on visual images was proposed. In this study, Hy-Line Gray chickens were bred as objects. Through manual marking, the training set, validation set and test set were established, and YOLO v3 model was adopted to detect the collected images. The value of subdivision and batch size were determined by experiment. The learning rate was dynamically adjusted according to the change of loss value in the training model. Finally, the mean average precision of the trained model on the validation set was 92.09%. In this paper, the recognition rates of six kinds of behaviors in the morning and in the afternoon and under different densities were analyzed. Furthermore, a kind of welfare indicator was tested and abnormal behavior was evaluated. The results showed that: The mean precision rate of the six behaviors was followed by mating (94.72%), stand (94.57%), feed (93.10%), spread (92.02%), fight (88.67%) and drink (86.88%). The mean false rate ranged from low to high was spread (0.11%), mating (0.14%), fight (0.20%), drink (0.25%), feed (1.17%) and stand (8.62%). The mean missing rate ranged from low to high was mating (4.65%), stand (5.01%), feed (5.15%), spread (6.25%), fight (14.69%) and drink (15.79%). The method presented in this paper has a good effect on identifying the behavior of egg breeders, which can provide technical support for the promotion of the self-breeding mode.