Interactive Dairy Goat Image Segmentation for Precision Livestock Farming

SIMPLE SUMMARY: Deep-learning-based algorithms have achieved great success in intelligent dairy goat farming. However, these algorithms require a large load of image annotation to obtain a decent performance. The existing annotation of dairy goat images heavily relies on non-intelligent tools such as Labelme, which makes it extremely inefficient and time-consuming to obtain a high-quality annotation result, hindering the application and development of deep-learning algorithms in intelligent dairy goat farming. In this study, we explore an interactive annotation method based on deep-learning algorithms for dairy goat image annotation, which significantly reduces the annotation workload of the user. Specifically, it only takes 7.12 s on average to annotate a dairy goat image with our developed annotation tool, five times faster than Labelme, which takes an average of 36.34 s per instance. ABSTRACT: Semantic segmentation and instance segmentation based on deep learning play a significant role in intelligent dairy goat farming. However, these algorithms require a large amount of pixel-level dairy goat image annotations for model training. At present, users mainly use Labelme for pixel-level annotation of images, which makes it quite inefficient and time-consuming to obtain a high-quality annotation result. To reduce the annotation workload of dairy goat images, we propose a novel interactive segmentation model called UA-MHFF-DeepLabv3+, which employs layer-by-layer multi-head feature fusion (MHFF) and upsampling attention (UA) to improve the segmentation accuracy of the DeepLabv3+ on object boundaries and small objects. Experimental results show that our proposed model achieved state-of-the-art segmentation accuracy on the validation set of DGImgs compared with four previous state-of-the-art interactive segmentation models, and obtained 1.87 and 4.11 on mNoC@85 and mNoC@90, which are significantly lower than the best performance of the previous models of 3 and 5. Furthermore, to promote the implementation of our proposed algorithm, we design and develop a dairy goat image-annotation system named DGAnnotation for pixel-level annotation of dairy goat images. After the test, we found that it just takes 7.12 s to annotate a dairy goat instance with our developed DGAnnotation, which is five times faster than Labelme.