Computer vision system for superpixel classification and segmentation of sheep

This paper presents an experiment with four different convolutional neural networks architectures that aim to classify segments of a sheep using a dataset of superpixels. The proposal used an image dataset with 512 images of 32 sheep. In this dataset of images, we applied the Simple Linear Iterative Clustering technique with a K number parameter of 600 to generate the dataset of superpixels that was later processed in the deep neural networks. We selected four architectures for training the models: VGG16, ResNet152V2, InceptionV3, and DenseNet201. The experiment was conducted using cross-validation with five-folds separating the dataset into training (60%), validation (20%), and testing (20%). The best result presented was from the approach with the DenseNet201 technique with an F-score of 0.928. When applying ANOVA, the P-value was 0.0000000000329 (3.29e-11***) between the tested architectures, which shows that the results are statistically significant. Therefore, DenseNet201 presented itself as a relevant architecture for this problem that aims to classify the superpixels referring to a sheep and the image's background, and the average IoU with post-processing for image segmentation with DenseNet201 obtained 0.8332. Thus, we can highlight the contributions of this research as a methodology to segment images of mixed-breed sheep of the Texel and Santa Inês breeds using convolutional neural networks and provide a non-invasive method that can support other implementations such as animal tracking and weight prediction. •Superpixel classification of miscegenated male and female sheep.•Evaluation of four architectures of CNNs for classification of superpixels created with the SLIC method.•Dataset of superpixels created with images of mixed-breed Texel and Santa Inês sheep.•DenseNet201 proved to be the best model for the segmentation task.