At present, the classical semantic segmentation methods can not accurately realize the obstacle instance detection of traffic scene, which makes this method can not be used in driverless system alone. In order to meet the requirements of obstacle detection accuracy in unmanned system, pixel level obstacle detection in complex driving scene is studied in this paper. A pixel level obstacle anomaly detection framework is formed by combining the maximum entropy, maximum distance and perceptual difference of uncertainty mapping with the output of dissimilar model. The framework uses uncertainty map and designs a hollow spatial pyramid pool structure of different receptive field stitching to enhance the correlation between various levels of information to improve the existing re synthesis methods to find the differences between the input image and the generated image. As a general framework, this method focuses on the trained segmentation network to ensure anomaly detection without affecting the accuracy of segmentation. The network is implemented based on pytorch framework. The experimental results on the road scene dataset cityscapes dataset show that the mlou reaches 85.7%.
The depth map contains spatial location information, which has been proven to be beneficial for RGBD salient object detection. However, the quality of the depth map will directly affect the accuracy of RGBD saliency detection. In order to solve the problem of different quality of depth maps, a detection model based on depth enhancement is designed to enhance the available depth information and filter low-quality depth maps. By comparing four evaluation indicators with five advanced models, the experimental results show that the model in this paper is advanced.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.