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Pig instance segmentation is a critical component of smart pig farming, serving as the basis for advanced applications such as health monitoring and weight estimation. However, existing methods typically rely on large volumes of precisely labeled mask data, which are both difficult and costly to obtain, thereby limiting their scalability in real-world farming environments. To address this challenge, this paper proposes a novel approach that leverages simpler box annotations as supervisory information to train a pig instance segmentation network. In contrast to traditional methods, which depend on expensive mask annotations, our approach adopts a weakly supervised learning paradigm that reduces annotation cost. Specifically, we enhance the loss function of an existing weakly supervised instance segmentation model to better align with the requirements of pig instance segmentation. We conduct extensive experiments to compare the performance of the proposed method that only uses box annotations, with that of five fully supervised models requiring mask annotations and two weakly supervised baselines. Experimental results demonstrate that our method outperforms all existing weakly supervised approaches and three out of five fully supervised models. Moreover, compared with fully supervised methods, our approach exhibits only a 3% performance gap in mask prediction. Given that annotating a box takes merely 26 seconds, whereas annotating a mask requires 94 seconds, this minor accuracy trade-off is practically negligible. These findings highlight the value of employing box annotations for pig instance segmentation, offering a more cost-effective and scalable alternative without compromising performance. Our work not only advances the field of pig instance segmentation but also provides a viable pathway to deploy smart farming technologies in resource-limited settings, thereby contributing to more efficient and sustainable agricultural practices.

A clear understanding of the number of pigs plays a crucial role in breeding management. Computer vision technology possesses several advantages, as it is harmless and labour-saving compared to traditional counting methods. Nevertheless, the existing methods still face some challenges, such as: (1) the lack of a substantial high-precision pig-counting dataset; (2) creating a dataset for instance segmentation can be time-consuming and labor-intensive; (3) interactive occlusion and overlapping always lead to incorrect recognition of pigs; (4) existing methods for counting such as object detection have limited accuracy. To address the issues of dataset scarcity and labor-intensive manual labeling, we make a semi-auto instance labeling tool (SAI) to help us to produce a high-precision pig counting dataset named Count1200 including 1220 images and 25,762 instances. The speed at which we make labels far exceeds the speed of manual annotation. A concise and efficient instance segmentation model built upon several novel modules, referred to as the Instances Counting Network (ICNet), is proposed in this paper for pig counting. ICNet is a dual-branch model ingeniously formed of a combination of several layers, which is named the Parallel Deformable Convolutions Layer (PDCL), which is trained from scratch and primarily composed of a couple of parallel deformable convolution blocks (PDCBs). We effectively leverage the characteristic of modeling long-range sequences to build our basic block and compute layer. Along with the benefits of a large effective receptive field, PDCL achieves a better performance for multi-scale objects. In the trade-off between computational resources and performance, ICNet demonstrates excellent performance and surpasses other models in Count1200, AP of 71.4% and AP50 of 95.7% are obtained in our experiments. This work provides inspiration for the rapid creation of high-precision datasets and proposes an accurate approach to pig counting.