A DEEP LEARNING APPROACH FOR COW UDDER DETECTION

Abstract

Image recognition with deep learning focuses on identifying one or more specific objects, class or feature in a specified image or video frame. Deep learning has received a lot of attention recently and is yielding unprecedented results. Image recognition is used in different applications, such as Medical diagnosis, Automatic inspection of manufacturing products and tasks like pedestrian detection. In the dairy industry, teat spraying is essential in order to prevent the spread of diseases like mastitis. However, the current automatic teat spraying/cup attachment methods have a lot of issues. For example, teat location coordinates are assumed to be fixed and are pre-stored while in reality, it is more likely that the cow might be more mobile, hence it is a flawed approach. Secondly, IR based systems are inconsistent due to the light absorption property of IR which has no guidance for exact detection. To address this problem, it is required to attach the cup or spray the teats only when the udder is detected. This thesis makes use of deep learning for the detection of cow udder which can be of great importance in the dairy industry which is currently relying on conventional methods that are inefficient and inconsistent. Though Deep Convolutional Neural Network is the most advanced technique for image recognition but it demands a great deal of training time and computational power. Focusing on the problem of image recognition with limited time for training and restricted computing power in mind, a technique called “Transfer Learning” is used in this thesis. This technique makes use of the pre-trained model in order to accelerate the process of learning. Trainable parameters from Xception Deep Convolution Neural Network (DCNN) model were transferred for identifying cow udder. The model training was done in three stages; the data collection or dataset building stage which includes acquiring cow udder images and manual segmentation then training stage and finally the validation/testing phase. The model is trained on our custom made cow udder dataset. With overall miou_1.0 of 0.88. The model was successful in accurately detecting udder.