Matches in SemOpenAlex for { <https://semopenalex.org/work/W3175779783> ?p ?o ?g. }
Showing items 1 to 89 of
89
with 100 items per page.
- W3175779783 endingPage "11" @default.
- W3175779783 startingPage "1" @default.
- W3175779783 abstract "Image segmentation is an important research in image processing and machine vision in which automated driving can be seen the main application scene of image segmentation algorithms. Due to the many constraints of power supply and communication in in-vehicle systems, the vast majority of current image segmentation algorithms are implemented based on the deep learning model. Despite the ultrahigh segmentation accuracy, the problem of mesh artifacts and segmentation being too severe is obvious, and the high cost, computational, and power consumption devices required are difficult to apply in real-world scenarios. It is the focus of this paper to construct a road scene segmentation model with simple structure and no need of large computing power under the premise of certain accuracy. In this paper, the ESPNet (Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation) model is introduced in detail. On this basis, an improved ESPNet model is proposed based on ESPNet. Firstly, the network structure of the ESPNet model is optimized, and then, the model is optimized by using a small amount of weakly labeled and unlabeled scene sample data. Finally, the new model is applied to video image segmentation based on dash cam. It is verified on Cityscape, PASCAL VOC 2012, and other datasets that the algorithm proposed in this paper is faster, and the amount of parameters required is less than 1% of other algorithms, so it is suitable for mobile terminals." @default.
- W3175779783 created "2021-07-05" @default.
- W3175779783 creator A5009866828 @default.
- W3175779783 creator A5025428746 @default.
- W3175779783 creator A5044527743 @default.
- W3175779783 creator A5083722494 @default.
- W3175779783 date "2021-06-28" @default.
- W3175779783 modified "2023-10-14" @default.
- W3175779783 title "The Segmentation of Road Scenes Based on Improved ESPNet Model" @default.
- W3175779783 cites W2031489346 @default.
- W3175779783 cites W2115579991 @default.
- W3175779783 cites W2124592697 @default.
- W3175779783 cites W2139225623 @default.
- W3175779783 cites W2275989239 @default.
- W3175779783 cites W2520760693 @default.
- W3175779783 cites W2531409750 @default.
- W3175779783 cites W2560023338 @default.
- W3175779783 cites W2735743304 @default.
- W3175779783 cites W2810974108 @default.
- W3175779783 cites W2901550124 @default.
- W3175779783 cites W2904485911 @default.
- W3175779783 cites W2913425791 @default.
- W3175779783 cites W2962772649 @default.
- W3175779783 cites W2963125010 @default.
- W3175779783 cites W2963418739 @default.
- W3175779783 cites W2963954267 @default.
- W3175779783 cites W2966373160 @default.
- W3175779783 cites W2978321147 @default.
- W3175779783 cites W2981524957 @default.
- W3175779783 cites W3004061291 @default.
- W3175779783 cites W3038020296 @default.
- W3175779783 cites W3093844748 @default.
- W3175779783 doi "https://doi.org/10.1155/2021/1681952" @default.
- W3175779783 hasPublicationYear "2021" @default.
- W3175779783 type Work @default.
- W3175779783 sameAs 3175779783 @default.
- W3175779783 citedByCount "2" @default.
- W3175779783 countsByYear W31757797832023 @default.
- W3175779783 crossrefType "journal-article" @default.
- W3175779783 hasAuthorship W3175779783A5009866828 @default.
- W3175779783 hasAuthorship W3175779783A5025428746 @default.
- W3175779783 hasAuthorship W3175779783A5044527743 @default.
- W3175779783 hasAuthorship W3175779783A5083722494 @default.
- W3175779783 hasBestOaLocation W31757797831 @default.
- W3175779783 hasConcept C120665830 @default.
- W3175779783 hasConcept C121332964 @default.
- W3175779783 hasConcept C124504099 @default.
- W3175779783 hasConcept C142575187 @default.
- W3175779783 hasConcept C154945302 @default.
- W3175779783 hasConcept C199360897 @default.
- W3175779783 hasConcept C25694479 @default.
- W3175779783 hasConcept C31972630 @default.
- W3175779783 hasConcept C41008148 @default.
- W3175779783 hasConcept C65885262 @default.
- W3175779783 hasConcept C75608658 @default.
- W3175779783 hasConcept C89600930 @default.
- W3175779783 hasConceptScore W3175779783C120665830 @default.
- W3175779783 hasConceptScore W3175779783C121332964 @default.
- W3175779783 hasConceptScore W3175779783C124504099 @default.
- W3175779783 hasConceptScore W3175779783C142575187 @default.
- W3175779783 hasConceptScore W3175779783C154945302 @default.
- W3175779783 hasConceptScore W3175779783C199360897 @default.
- W3175779783 hasConceptScore W3175779783C25694479 @default.
- W3175779783 hasConceptScore W3175779783C31972630 @default.
- W3175779783 hasConceptScore W3175779783C41008148 @default.
- W3175779783 hasConceptScore W3175779783C65885262 @default.
- W3175779783 hasConceptScore W3175779783C75608658 @default.
- W3175779783 hasConceptScore W3175779783C89600930 @default.
- W3175779783 hasFunder F4320321001 @default.
- W3175779783 hasLocation W31757797831 @default.
- W3175779783 hasOpenAccess W3175779783 @default.
- W3175779783 hasPrimaryLocation W31757797831 @default.
- W3175779783 hasRelatedWork W134976887 @default.
- W3175779783 hasRelatedWork W1669643531 @default.
- W3175779783 hasRelatedWork W1982826852 @default.
- W3175779783 hasRelatedWork W2005437358 @default.
- W3175779783 hasRelatedWork W2274529912 @default.
- W3175779783 hasRelatedWork W2384989255 @default.
- W3175779783 hasRelatedWork W2517104666 @default.
- W3175779783 hasRelatedWork W2549936415 @default.
- W3175779783 hasRelatedWork W2566648451 @default.
- W3175779783 hasRelatedWork W1967061043 @default.
- W3175779783 hasVolume "2021" @default.
- W3175779783 isParatext "false" @default.
- W3175779783 isRetracted "false" @default.
- W3175779783 magId "3175779783" @default.
- W3175779783 workType "article" @default.