Custom Object Detection with Deep Learning and Synthetic Datasets

Abstract

Detecting and localizing tree trunks is a necessary component for automatic harvesting machines. This task can be split into two sub-tasks: object detection and depth estimation. Object detection is a crucial problem that appears often in the field of computer vision and robotics. The advancements of deep neural networks have been making remarkable improvements for object detection systems. However, deep learning methods require a large amount of data, making custom objects that are not annotated in massive public datasets diffcult to detect. This work presents an attempt on addressing the problem of data shortage by proposing a method to construct synthetic datasets automatically for the custom objects, and then quantitatively examines the performance of state-of-the-art deep learning models on these synthetic datasets. Furthermore, the depth information of the detected objects is also estimated from stereo images and mapped onto the detected objects. The results show that the synthetic datasets can be used to train the neural networks to detect visually correct tree trunks on unseen new images. The resulting software can be used for any given custom objects with the process similar to that of tree trunks.