With the vigorous development of deep learning and the widespread use of mobile robots, automatic driving has gradually become a research hotspot. Environment perception is the most important part of automatic driving technology, and the purpose of environment perception is to distinguish the environmental content. Therefore, accurate and efficient image semantic segmentation method is becoming more and more important. In this paper, we introduce a deep semantic segmentation solution: IIE-SegNet: Deep semantic segmentation network with enhanced boundary based on image information entropy. At present, deep learning based on semantic segmentation solutions has some problems, such as low segmentation accuracy for small-scale objects and unclear boundary of segmented objects. Our method preserves the boundary of the segmentation object, and has higher segmentation accuracy for small-scale objects. In our method, the features of the underlying pooling layer are added to the ASPP structure of the encoding module, and the image information entropy of the previous pooling layers is introduced into the decoding module. We also introduce focal loss to solve the problem of imbalance between positive and negative samples. Finally, the test results on the extended Pascal VOC 2012 test set, abbreviated to Exp-Pascal VOC 2012 test set show that the proposed method has better performance on Exp-Pascal VOC 2012 test set compared with the advanced methods at the present stage, the segmentation accuracy of small-scale targets is higher, and the boundary is clearer.

A stereo vision system provides important support for underwater robots to achieve autonomous navigation, obstacle avoidance, and precise operation in complex underwater environments. This article proposes an unsupervised underwater stereo matching method based on semantic attention. By combining deep learning and semantic information, it fills the challenge of insufficient training data, enhances the intelligence level of underwater robots, and promotes the progress of underwater scientific research and marine resource development. This article proposes an underwater unsupervised stereo matching method based on semantic attention, targeting the missing training supervised dataset for underwater stereo matching. An adaptive double quadtree semantic attention model for the initial estimation of semantic disparity is designed, and an unsupervised AWLED semantic loss function is proposed, which is more robust to noise and textureless regions. Through quantitative and qualitative evaluations in the underwater stereo matching dataset, it was found that D1 all decreased by 0.222, EPE decreased by 2.57, 3px error decreased by 1.53, and the runtime decreased by 7 ms. This article obtained advanced results.

This paper describes a method that addresses the transient loss of observations in sea surface target state estimations. A six degrees of freedom swing platform fixed with a MiniRadaScan is used to simulate the loss of observations. The state transition model based on the historical observation data fit prediction is designed because the existing state estimation method can only use the system model prediction while the observation is missing. An observation data sliding window width adaptive adjustment strategy is proposed that can improve the fitting accuracy of the state transition model. To solve the problem where the weight value of the Gaussian components of the Gaussian mixture filter is not changed in the time update stage while the observation is missing, an adaptive adjustment strategy for the weight is proposed based on the Chapman‐Kolmogorov equation, which can improve the estimation precision under the conditions of the missing observation. The simulation test demonstrates the proposed accuracy and real‐time performance of the proposed algorithm.