Enhanced Mask R-CNN for herd segmentation

Livestock image segmentation is an important task in the field of vision and image processing. Since utilizing the concentration of forage in the grazing area with shielding the surrounding farm plants and crops is necessary for making effective cattle ranch arrangements, there is a need for a segmentation method that can handle multiple objects segmentation. Moreover, the indistinct boundaries and irregular shapes of cattle bodies discourage the application of the existing Mask Region-based Convolutional Neural Network (Mask R-CNN) which was primarily modeled for the segmentation of natural images. To address this, an enhanced Mask R-CNN model was proposed for multiple objects instance segmentation to support indistinct boundaries and irregular shapes of cattle bodies for precision livestock farming. The contributions of this method are in multiple folds: 1) optimal filter size smaller than a residual network for extracting smaller and composite features; 2) region proposals for utilizing multiscale semantic features; 3) Mask R-CNN's fully connected layer integrated with sub-network for an enhanced segmentation. The experiment conducted on pre-processed datasets produced a mean average precision (mAP) of 0.93, which was higher than the results from the existing state-of-the-art models.