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- W4361761538 abstract "In order to solve a series of problems of slow speed and low accuracy encountered by traditional infrared pedestrian detection methods, we improved the YOLOX detection network and proposed a new model—G-YOLOX. In order to extract deeper information from the image, we changed the backbone of YOLOX from CSPDarknet to Ghostnet. The CBAM is added to Ghost Bottleneck which is the base block of Ghostnet, and the original activation function ReLU is replaced by Leaky ReLU to improve the rate of convergence. After the above improvement, the parameters of G-YOLOX model is reduced by 80% and the training speed of the model was significantly improved. The G-YOLOX model realizes lightweight. The study results show that the G-YOLOX network improves the detection speed of the model by 43%, its fps increases from 17 to 30, and its AP (@0.5) is 2.35% higher than that of YOLOX." @default.
- W4361761538 created "2023-04-04" @default.
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- W4361761538 date "2023-01-01" @default.
- W4361761538 modified "2023-09-26" @default.
- W4361761538 title "An Infrared Pedestrian Detection Method Based on Improved YOLOX" @default.
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- W4361761538 doi "https://doi.org/10.1007/978-981-99-0923-0_64" @default.
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