BEV high-definition maps play a crucial role in autonomous driving and navigation systems, where their segmentation accuracy directly affects system performance and safety. Traditional feature extraction networks, when dealing with complex BEV maps, are often limited by their fixed kernel sizes and shapes, leading to insufficient accuracy in critical tasks such as lane segmentation. This paper improves and proposes an A-HDN framework for high-definition segmentation in BEV, observing the slender and continuous features of linear structures and introducing a Dynamic Serpentine Convolution network (DSConv). This network can flexibly conform to the lane structures in BEV and learn features, while also staying close to the target structure under constraints, thus better learning the features. Additionally, a Ghost module is introduced, which allows the learning network to better preserve features without affecting model performance. Finally, experiments show that this algorithm has increased the map segmentation precision by 1.6 IoU and improved directional detection by 0.8 mAP.
KEYWORDS: Standards development, Data modeling, Information technology, Visualization, Logic devices, Logic, Information science, Error analysis, Distance measurement, Analytical research
To save costs of manual reviewing, a tool was designed for automatically checking academic integrity and content consistency of abstracts through analyzing the academic problems of abstracts from multi-dimensional testing indicators. First of all, the recognition of abstract knowledge elements can be achieved with the help of the Naive Bayes algorithm and the posterior probability correction method. On this basis, the consistency between the abstract and the text can be checked in combination with semantic matching and knowledge element matching. As can be seen from the experimental results, the F value of the model can reach 0.85 in the academic integrity checking of abstracts. At the same time, the checking granularity is refined to effectively distinguish the abstracts of varying quality.
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