Up-to-date High-Definition (HD) maps are essential for self-driving cars. To achieve constantly updated HD maps, we present a deep neural network (DNN), Diff-Net, to detect changes in them. Compared to traditional methods based on object detectors, the essential design in our work is a parallel feature difference calculation structure that infers map changes by comparing features extracted from the camera and rasterized images. To generate these rasterized images, we project map elements onto images in the camera view, yielding meaningful map representations that can be consumed by a DNN accordingly. As we formulate the change detection task as an object detection problem, we leverage the anchor-based structure that predicts bounding boxes with different change status categories. To the best of our knowledge, the proposed method is the first end-to-end network that tackles the high-definition map change detection task, yielding a single stage solution. Furthermore, rather than relying on single frame input, we introduce a spatio-temporal fusion module that fuses features from history frames into the current, thus improving the overall performance. Finally, we comprehensively validate our method's effectiveness using freshly collected datasets. Results demonstrate that our Diff-Net achieves better performance than the baseline methods and is ready to be integrated into a map production pipeline maintaining an up-to-date HD map.

Face detection is a hot topic in computer vision. The face detection methods usually consist of two subtasks, i.e. the classification subtask and the regression subtask, which are trained with different samples. However, current face detection knowledge distillation methods usually couple the two subtasks, and use the same set of samples in the distillation task. In this paper, we propose a task decoupled knowledge distillation method, which decouples the detection distillation task into two subtasks and uses different samples in distilling the features of different subtasks. We firstly propose a feature decoupling method to decouple the classification features and the regression features, without introducing any extra calculations at inference time. Specifically, we generate the corresponding features by adding task-specific convolutions in the teacher network and adding adaption convolutions on the feature maps of the student network. Then we select different samples for different subtasks to imitate. Moreover, we also propose an effective probability distillation method to joint boost the accuracy of the student network. We apply our distillation method on a lightweight face detector, EagleEye. Experimental results show that the proposed method effectively improves the student detector's accuracy by 5.1%, 5.1%, and 2.8% AP in Easy, Medium, Hard subsets respectively.