An efficient anchor‐free method for pig detection

Abstract

Given the rapid growth of commercial pig farms, the need to automatically monitor pig behaviour becomes more important in order to assist farmers. Recent advances in convolutional neural networks may pave the way for new solutions. However, the primary task of individual pig detection under real-world conditions is still a challenging task. Previous studies used anchor-based frameworks that are unsuitable for such crowded scenarios with extreme overlapping. Furthermore, most applications focus on specific levels of brightness, farm facilities, or pig species without considering generalization. To tackle these problems, an anchor-free pig detection method based on pig centre localization is first proposed. Then, a novel negative training data augmentation technique is introduced using examples from outside the training distribution. Furthermore, using the test time augmentation technique is proposed to improve the model performance. Experiments are conducted on two online pig detection datasets; the network surpasses state-of-the-art results for both datasets. It is also found that the proposed method outperforms the latest anchor-free techniques commonly used in crowded scenarios. The method can detect pigs individually, even if their bounding boxes overlap strongly or occlude each other. Moreover, the real-time system achieves an improvement of 10% in F measure $F_{\text{measure}}$ when testing in unconstrained real-world conditions.