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• W4288045170 endingPage "111655" @default.
• W4288045170 startingPage "111655" @default.
• W4288045170 abstract "For autonomous driving systems, vehicle detection is an important part and challenging problem due to the complex traffic scenes and poor computing resources. This paper proposes an improved SSD(single shot mutlibox detector) algorithm for the fast detection of vehicles in traffic scenes. MobileNet v2 is selected as the backbone feature extraction network for SSD, which improves the real-time performance of the algorithm. To improve detection accuracy, the channel attention mechanism is utilized for feature weighting, and the deconvolution module is utilized to construct a bottom–top feature fusion structure. The experimental results show that the average precision of the proposed algorithm on the BDD100K and KITTI datasets is 82.59% and 84.83%, respectively. The single inference time of the algorithm is 73ms, which is only about 5/11 of the original model, realizing the improvement of inference speed and prediction accuracy." @default.
• W4288045170 created "2022-07-27" @default.
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• W4288045170 date "2022-09-01" @default.
• W4288045170 modified "2023-10-18" @default.
• W4288045170 title "Fast vehicle detection algorithm in traffic scene based on improved SSD" @default.
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• W4288045170 doi "https://doi.org/10.1016/j.measurement.2022.111655" @default.
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