Automated Iterative Training of Convolutional Neural Networks for Tree Skeleton Segmentation

Training of convolutional neural networks for semantic segmentation requires accurate pixel-wise labeling. Depending on the application this can require large amounts of human effort. The human-in-the-loop method reduces labeling effort but still requires human intervention for a selection of images. This paper describes a new iterative training method: Automating-the-loop. Automating-the-loop aims to replicate the human adjustment in human-in-the-loop, with an automated process. Thereby, removing human intervention during the iterative process and drastically reducing labeling effort. Using the application of segmented apple tree detection, we compare human-in-the-loop, Self Training Loop, Filtered-Self Training Loop (semi-supervised learning) and our proposed method automating-the-loop. These methods are used to train U-Net, a deep learning based convolutional neural network. The results are presented and analyzed on both traditional performance metrics and a new metric, Horizontal Scan. It is shown that the new method of automating-the-loop greatly reduces the labeling effort while generating a network with comparable performance to both human-in-the-loop and completely manual labeling.