Traffic sign detection plays an important role in intelligent transportation systems. But traffic signs are still not well-detected by deep convolution neural network-based methods because the sizes of their feature maps are constrained, and the environmental context information has not been fully exploited by other researchers. What we need is a way to incorporate relevant context detail from the neighboring layers into the detection architecture. We have developed a novel traffic sign detection approach based on recurrent attention for multi-scale analysis and use of local context in the image. Experiments on the German traffic sign detection benchmark and the Tsinghua-Tencent 100K data set demonstrated that our approach obtained an accuracy comparable to the state-of-the-art approaches in traffic sign detection.

Digital dentistry has received more attention in the past decade. However, current deep learning-based methods still encounter difficult challenges. The proposal-based methods are sensitive to the localization results due to the lack of local cues, while the proposal-free methods have poor clustering outputs because of the affinity measured by the low-level characteristics, especially in situations of tightly arranged teeth. In this article, we present a novel proposal-based approach to combine objectness and pointwise knowledge in an attention mechanism for point cloud-based tooth instance segmentation, using local information to improve 3D proposal generation and measuring the importance of local points by calculating the center distance. We evaluate the performance of our approach by constructing a Shining3D tooth instance segmentation dataset. The experimental results verify that our approach gives competitive results when compared with the other available approaches.