The presence of occlusions has provided substantial challenges to typically-powerful object recognition algorithms. Additional sources of information can be extremely valuable to reduce errors caused by occlusions. Scene context is known to aid in object recognition in biological vision. In this work, we attempt to add robustness into existing Region Proposal Network-Deep Convolutional Neural Network (RPN-DCNN) object detection networks through two distinct scene-based information fusion techniques. We present one algorithm under each methodology: the first operates prior to prediction, selecting a custom object network to use based on the identified background scene, and the second operates after detection, fusing scene knowledge into initial object scores output by the RPN. We demonstrate our algorithms on challenging datasets featuring partial occlusions, which show overall improvement in both recall and precision against baseline methods. In addition, our experiments contrast multiple training methodologies for occlusion handling, finding that training on a combination of both occluded and unoccluded images demonstrates an improvement over the others. Our method is interpretable and can easily be adapted to other datasets, offering many future directions for research and practical applications.

翻译：遮挡现象对通常强大的物体识别算法构成了显著挑战。利用额外信息源对于减少由遮挡引起的错误极为重要。已知在生物视觉中，场景上下文有助于物体识别。在本研究中，我们尝试通过两种不同的基于场景的信息融合技术，为现有的区域提议网络-深度卷积神经网络（RPN-DCNN）物体检测网络增加鲁棒性。我们针对每种方法提出一种算法：第一种在预测前运行，根据识别出的背景场景选择定制的物体网络；第二种在检测后运行，将场景知识融合到RPN输出的初始物体分数中。我们在包含部分遮挡的挑战性数据集上验证了算法，结果显示在召回率和精确度上均较基线方法有整体提升。此外，我们的实验对比了多种处理遮挡的训练方法，发现同时使用遮挡与非遮挡图像进行训练的效果优于其他方法。本方法具有可解释性，易于适配其他数据集，为未来研究和实际应用提供了多种可能方向。