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FCN[14]
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Proposes novel end-to-end networkarchitecture ; Encoder-decoderarchitec-ture ; Fully connected output classification. |
Images of any size can be split. |
The large number of parameters and the pooling opera-tion caused a loss of spatial information in the images and a low accuracy rate. |
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SegNet[15]
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Symmetrical Encoder-Decoder architecture ; up-sampling to recover im-age size at the decoding stage using unpool-ing; full convolutional layer output classification. |
The small number of parameters compared to FCN maintains the integrity of the HF information. |
The computational effort is too large to meet the real-time requirements of lane detection. The up-sampling operation also loses adjacent informa-tion. |
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Unet[16]
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Symmetrical structure; co-nnects each stage to the encoder feature map with the upsampled feature map of the decoder. |
Can be trained end-to-end from very small data sets; fast. |
More suitable for segmentation of medical images |
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ENet[17]
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Consisting of Bottleneck mod-ules; with a large encoder-small decoder st-ructure. |
Greatly reduces the nu-mber of parameters and floating point operations, takes up less memory and has high real time performance. |
Increases the number of calls to the kernel function; not very precise and unstable results. |
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PSPNet[18]
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Improving ResNet structures using null conv-olution ; A pyramid pooling module has been ad-ded. |
The segmentation acc-uracy exceeds that of models such as FCN, DPN and CRF-RNN. |
Obscured situations between targets are not handled well and the edges are not seg-mented accurately enough. |
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ERFNet[19]
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ENet network improve-ments; the adoption of factorized convolutions; |
Non-bottleneck is more accurate to bottleneck. |
High calculation volume compared to Enet. |
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DeepLab V3+[20]
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Uses a modified version of Xception as the base network; uses atrous[19] convolutional kernels. |
More accurate segmentation of target edges; considers global information, eliminates noise interference and imp-roves segmentation accuracy. |
The model does not run at a high speed and has a high storage space requi-rement. |
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FPN[21]
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Combining FCN and Mask R-CNN[13] using rich multi-scale features. |
Semantic segmentation and instance segmentation tasks can be solved simultaneously. |
Increased inference time; larger memory footprint; use of image pyramids only in the testing phase. |
