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While deep learning is a very popular subsection of machine learning and has been used for decades, an availability gap exists for both knowledge and datasets in unstructured environments or in-the-wild applications. Knowledge of mobility in these free environments is an important stepping-stone for both Department of Defense applications as well as industrial autonomy applications. A few datasets that exist for unstructured environments such as RELLIS-3D for robotics, RUGD for navigation, and GOOSE for perception; however, due to the limited selection of datasets for this type of environment, most deep learning algorithms have not been thoroughly tested on this scenario. In this article, we will implement multiple deep learning methods on an in-house dataset to evaluate performance. Specifically, this article investigates the performance of pretrained, publicly available YOLOv4, ResNet-50, and Single Shot Detector (SSD) models on detection of unknown object classes encountered in the wild for improved, safe, and reliable maneuverability with minimized impediment in unstructured environments. The models used are tested using a dataset developed in-house for unstructured environment studies, and their performance is assessed with multiple metrics. The data used in this experiment was collected by the United States Army Corps of Engineers Engineer Research and Development Center.
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