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• W2996106355 abstract "Semantic image segmentation plays a pivotal role in many vision applications including autonomous driving and medical image analysis. Most of the former approaches move towards enhancing the performance in terms of accuracy with a little awareness of computational efficiency. In this paper, we introduce LiteSeg, a lightweight architecture for semantic image segmentation. In this work, we explore a new deeper version of Atrous Spatial Pyramid Pooling module (ASPP) and apply short and long residual connections, and depthwise separable convolution, resulting in a faster and efficient model. LiteSeg architecture is introduced and tested with multiple backbone networks as Darknet19, MobileNet, and ShuffleNet to provide multiple trade-offs between accuracy and computational cost. The proposed model LiteSeg, with MobileNetV2 as a backbone network, achieves an accuracy of 67.81% mean intersection over union at 161 frames per second with 640 × 360 resolution on the Cityscapes dataset." @default.
• W2996106355 created "2019-12-26" @default.
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• W2996106355 date "2019-12-01" @default.
• W2996106355 modified "2023-09-29" @default.
• W2996106355 title "LiteSeg: A Novel Lightweight ConvNet for Semantic Segmentation" @default.
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• W2996106355 doi "https://doi.org/10.1109/dicta47822.2019.8945975" @default.
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