Accuracy Improvement Methods to a Deep Neural Network Model in Computer Vision

This paper covers machine learning approaches to improve the accuracy of a Deep Neural Network (DNN) inference in computer vision, particularly in image segmentation. A few benefits of object recognition aided by computer vision is to help aircraft crew avoid adversaries in various weather conditions, to offload the crew's attention from finding desired objects as well as estimating the position of those objects throughout missions. Since DNN inference is trained without explicit programming, it is challenging to precisely locate its sub-optimality when it performs less accurate prediction. This paper explores three machine learning approaches that improve a DNN model prediction accuracy: 1) Prediction result analysis using the author's original work called Directional Pixel Delta as well as Jaccard's Intersection over Union (IoU) loss function 2) Bayesian optimization hyper-parameter tuning 3) Training data optimization via Bootstrap Aggregating.