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• W2999691331 abstract "Object segmentation of monitor systems based on the Internet of drones plays an important role in the practical applications of wide-area smart-city intelligent monitoring systems. It is an important step for extracting objects from remote-sensing images, and provides a reliable theoretical basis for key property monitoring, environmental monitoring, disaster monitoring, and agricultural monitoring. To improve the accuracy of object segmentation and to solve the problem of inadequate edge recognition, a joint-learning segmentation scheme was designed that combines the conditional random field (CRF) model with an improved U-net model. It employs the improved U-net model as the front-end model of the joint-learning framework for feature fusion and the CRF model as the back-end of the joint-learning framework for transforming to gradient optimization-based recurrent neural networks. The joint-learning framework enables the front and back parts to interact with each other to obtain the location of the target and its classification information accurately. The joint-learning framework was realized on open datasets and compared with state-of-the-art remote-sensing image segmentation algorithms. The experiment results show that the accuracy of the ground object segmentation improved to 86.1%, which is an encouraging improvement." @default.
• W2999691331 created "2020-01-23" @default.
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• W2999691331 date "2020-02-01" @default.
• W2999691331 modified "2023-10-15" @default.
• W2999691331 title "Joint-learning segmentation in Internet of drones (IoD)-based monitor systems" @default.
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• W2999691331 doi "https://doi.org/10.1016/j.comcom.2020.01.027" @default.
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