论文标题
具有压缩图像数据的强大深度神经对象检测和对汽车驾驶场景的细分
Robust Deep Neural Object Detection and Segmentation for Automotive Driving Scenario with Compressed Image Data
论文作者
论文摘要
深度神经对象检测或分割网络通常是通过原始的，未压缩的数据训练的。但是，在实际应用中，输入图像通常通过用于有效传输数据的压缩来恶化。因此，我们建议在训练过程中添加恶化的图像，以更快地提高两个最先进网络的鲁棒性和掩盖R-CNN。在整个论文中，我们通过评估了即将进行的视频编码标准Versatile视频编码（VVC），研究了CityScapes数据集上新训练的模型（VVC），调查了一个自主驾驶方案。当使用已提出的方法训练的模型时，对于压缩输入图像，R-CNN的加权平均精度最高可提高3.68个百分点，这对应于近48％的比特率节省。
Deep neural object detection or segmentation networks are commonly trained with pristine, uncompressed data. However, in practical applications the input images are usually deteriorated by compression that is applied to efficiently transmit the data. Thus, we propose to add deteriorated images to the training process in order to increase the robustness of the two state-of-the-art networks Faster and Mask R-CNN. Throughout our paper, we investigate an autonomous driving scenario by evaluating the newly trained models on the Cityscapes dataset that has been compressed with the upcoming video coding standard Versatile Video Coding (VVC). When employing the models that have been trained with the proposed method, the weighted average precision of the R-CNNs can be increased by up to 3.68 percentage points for compressed input images, which corresponds to bitrate savings of nearly 48 %.
