论文标题
物体检测中的闭塞碎片问题
The Problem of Fragmented Occlusion in Object Detection
论文作者
论文摘要
自然环境中的对象检测仍然是一项非常具有挑战性的任务，即使深度学习在过去几年中取得了巨大的改善。基于深度学习的对象检测的一个基本问题是，训练数据和建议的模型都不旨在挑战碎片的闭塞挑战。碎裂的遮挡比普通的部分闭塞更具挑战性，并且在森林等自然环境中经常发生。碎片碎片的一个激励例子是通过叶子检测对象检测，这是绿色边界监视的必不可少的要求。本文对最先进的检测器进行了分析，并提议在新的培训数据中训练面膜R-CNN，该数据明确捕获了碎片碎片的问题。结果显示，通过这种新的训练策略（也针对其他检测器），蒙版R-CNN明显改善，以显示稍微碎裂的闭塞。
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.
