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- W4313000528 abstract "For the detection of remote and fuzzy small targets, machine learning detection has a higher probability of missed detection. It is more dangerous when applied to vehicle detection. Therefore, this paper proposes and uses a detection method that combines machine learning and traditional methods to deal with the problem that the vehicle in front with a high risk factor is missed. Through the designed prediction mechanism for fusion, the video frame images will be processed by different modules. Compared with target detection using machine learning only, the missed detection rate was reduced by 29.1%. Finally, we deploy it in an embedded device." @default.
- W4313000528 created "2023-01-05" @default.
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- W4313000528 date "2022-07-15" @default.
- W4313000528 modified "2023-09-27" @default.
- W4313000528 title "Embedded front vehicle detection system based on fusion of machine learning and traditional methods" @default.
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- W4313000528 doi "https://doi.org/10.1109/icbaie56435.2022.9985918" @default.
- W4313000528 hasPublicationYear "2022" @default.
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