Dual attention-guided feature pyramid network for instance segmentation of group pigs.

• Dual attention feature pyramid was developed to instance segmentation of pigs. • Dual attention blocks were better than other state-of-art attention modules. • Visualization of attention maps suggested that it can capture semantic similarity. • Prediction of public datasets demonstrated the practicability of attention blocks. In respect of pig instance segmentation, the application of traditional computer vision techniques is constrained by sundries barrier, overlapping, and different perspectives in the pig breeding environment. In recent years, the attention-based methods have achieved remarkable performance. In this paper, we introduce two types of attention blocks into the feature pyramid network (FPN) (see nomenclature table) framework, which encode the semantic interdependencies in the channel (named channel attention block (CAB)) (see nomenclature table) and spatial (named spatial attention block (SAB)) (see nomenclature table) dimensions, respectively. By integrating the associated features, the CAB selectively emphasizes the interdependencies among the channels. Meanwhile, the SAB selectively aggregates the features at each position through a weighted sum of the features at all positions. A dual attention block (DAB) (see nomenclature table) is proposed to integrate CAB features with SAB information flexibly. A total of 45 pigs with 8 pens are captured as the experiment subjects. In comparison with such state-of-art attention modules as convolutional block attention module (CBAM) (see nomenclature table), bottleneck attention module (BAM) (see nomenclature table), and spatial-channel squeeze & excitation (SCSE) (see nomenclature table), embedding DAB can contribute to the most significant performance improvement in different task networks with distinct backbone networks. Especially with HTC-R101-DAB (hybrid task cascade) (see nomenclature table), the best performance is produced, with the AP 0.5 (average precision) (see nomenclature table) AP 0.75 , AP 0.5:0.95 , and AP 0.5:0.95-large reaching 93.1%, 84.1%, 69.4%, and 71.8%, respectively. Also, as indicated by ablation experiments, the SAB contributes more than CAB. Meanwhile, the predictive results appear a trend of increasing initially and decreasing afterwards after different numbers of SAB are merged. Besides, as revealed by the visualization of attention maps, attention blocks can extract regions with similar semantic information. The attention-based models also produce outstanding segmentation performance on public dataset, which evidences the practicability of our attention blocks. Our baseline models are available 1 1 https://github.com/zhiweihu1103/pig-instance-segmentation. [ABSTRACT FROM AUTHOR]