  Object detection in natural environments is still a very challenging task,
even though deep learning has brought a tremendous improvement in performance
over the last years. A fundamental problem of object detection based on deep
learning is that neither the training data nor the suggested models are
intended for the challenge of fragmented occlusion. Fragmented occlusion is
much more challenging than ordinary partial occlusion and occurs frequently in
natural environments such as forests. A motivating example of fragmented
occlusion is object detection through foliage which is an essential requirement
in green border surveillance. This paper presents an analysis of
state-of-the-art detectors with imagery of green borders and proposes to train
Mask R-CNN on new training data which captures explicitly the problem of
fragmented occlusion. The results show clear improvements of Mask R-CNN with
this new training strategy (also against other detectors) for data showing
slight fragmented occlusion.
