Depth sensing technology has become important in a number of consumer, robotics, and automated driving applications. However, the depth maps generated by such technologies today still suffer from limited resolution, sparse measurements, and noise, and require significant post-processing. Depth map data often has higher dynamic range than common 8-bit image data and may be represented as 16-bit values. Deep convolutional neural nets can be used to perform denoising, interpolation and completion of depth maps; however, in practical applications there is a need to enable efficient low-power inference with 8-bit precision. In this paper, we explore methods to process high-dynamic-range depth data using neural net inference engines with 8-bit precision. We propose a simple technique that attempts to retain signal-to-noise ratio in the post-processed data as much as possible and can be applied in combination with most convolutional network models. Our initial results using depth data from a consumer camera device show promise, achieving inference results with 8-bit precision that have similar quality to floating-point processing.
Udder ranking is one of the crucial traits and used extensively in cattle breeding. The analysis of the udder images is challenging due to the variations in the captured conditions of the non-rigid nature of the organ, the farm environment, and disturbances in the form of irrelevant segments of other cattle parts. To this end, we proposed a deep learning-based udder classification algorithm to enhance registrations’ precision within cattle breeding. We explore a convolution neural network (CNN), namely the VGG-16 model. The model is trained and validated on a cattle dataset that is collected in Norwegian dairy cattle farms. Expert technicians in the form manually annotate the dataset. We demonstrate that the VGG-16 model used as the backbone can efficiently give an acceptable performance with training and validation accuracy of 97% and 93% respectively on our custom dataset.