The Problem of Fragmented Occlusion in Object Detection
This work addresses a specific challenge in object detection for applications such as green border surveillance, but it is incremental as it adapts an existing method to new data.
The paper tackles the problem of fragmented occlusion in object detection, particularly in natural environments like forests, by proposing to train Mask R-CNN on new data that explicitly captures this challenge, resulting in clear improvements for data with slight fragmented occlusion.
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.