Developing computationally efficient semantic segmentation models that are suitable for resource-constrained mobile devices is an open challenge in computer vision research. To address this challenge, we propose a novel real-time semantic scene segmentation model called Multi-encoder Context Aggregation Network (MCANet), which offers the best combination of low model complexity and state-of-the-art (SOTA) performance on benchmark datasets. While we follow the multi-encoder approach, our novelty lies in the varying number of scales to capture both global context and local details effectively. We introduce suitable lateral connections between sub-encoders for improved feature refinement. We also optimize the backbone by exploiting the residual block of MobileNet for resource-constrained applications. On the decoder side, the proposed model includes a new Local and Global Context Aggregation (LGCA) module that significantly enhances semantic details in the segmentation output. Finally, we use several known efficient convolution techniques for the classification module to make the model more computationally efficient. We provide a comprehensive evaluation of MCANet on multiple datasets containing structured and unstructured urban street scenes. Among the existing real-time models with less than 3 million parameters, the proposed model is more competitive as it achieves the SOTA performance without ImageNet pre-trained weights on both structured and unstructured environments while being more compact for resource-constrained applications.

https://ieeexplore.ieee.org/ielx7/6287639/6514899/10164083.pdf

Multi-Encoder Context Aggregation Network for Structured and Unstructured Urban Street Scene Analysis

Developing computationally efficient semantic segmentation models that are suitable for resource-constrained mobile devices is an open challenge in computer vision research. To address this challenge, we propose a novel real-time semantic scene segmentation model called Multi-encoder Context Aggregation Network (MCANet), which offers the best combination of low model complexity and state-of-the-art (SOTA) performance on benchmark datasets. While we follow the multi-encoder approach, our novelty lies in the varying number of scales to capture both global context and local details effectively. We introduce suitable lateral connections between sub-encoders for improved feature refinement. We also optimize the backbone by exploiting the residual block of MobileNet for resource-constrained applications. On the decoder side, the proposed model includes a new Local and Global Context Aggregation (LGCA) module that significantly enhances semantic details in the segmentation output. Finally, we use several known efficient convolution techniques for the classification module to make the model more computationally efficient. We provide a comprehensive evaluation of MCANet on multiple datasets containing structured and unstructured urban street scenes. Among the existing real-time models with less than 3 million parameters, the proposed model is more competitive as it achieves the SOTA performance without ImageNet pre-trained weights on both structured and unstructured environments while being more compact for resource-constrained applications. 

/pdf/multi-encoder-context-aggregation-network-for-structured-and-1yp5sc9p.pdf

Multi-encoder Context Aggregation Network for Structured and Unstructured Urban Street Scene Analysis

Mixture lightweight transformer for scene understanding

For image semantic segmentation, a fully convolutional network is usually employed as the encoder to abstract visual features of the input image. A meticulously designed decoder is used to decoding the final feature map of the backbone. The output resolution of backbones which are designed for image classification task is too low to match segmentation task. Most existing methods for obtaining the final high-resolution feature map can not fully utilize the information of different layers of the backbone. To adequately extract the information of a single layer, the multi-scale context information of different layers, and the global information of backbone, we present a new attention-augmented module named Dense-attention Context Module (DCM), which is used to connect the common backbones and the other decoding heads. The experiments show the promising results of our method on Cityscapes dataset.

DCM: A Dense-Attention Context Module For Semantic Segmentation

The invention discloses a real-time image semantic segmentation method and system based on a lightweight convolutional neural network The system comprises a down-sampling unit, an up-sampling unit and an extreme efficient residual module The down-sampling unit reduces the resolution and changes the number of channels; the up-sampling unit improves the resolution and changes the number of channels; the extreme efficient residual error module is composed of 1D decomposition convolution, efficient depth separable convolution and expansion convolution with different expansion rates, and is usedfor extracting features The overall network architecture disclosed by the invention is an efficient asymmetric multi-branch encoding and decoding structure; compared with the existing most advanced lightweight network model, the network architecture and the segmentation method provided by the invention have the advantages that the optimal balance between the segmentation precision and the implementation efficiency is realized, and the network architecture and the segmentation method become an effective method for solving a real-time image semantic segmentation task

Real-time image semantic segmentation method and system based on lightweight convolutional neural network

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DCM: A Dense-Attention Context Module For Semantic Segmentation

Real-time image semantic segmentation method and system based on lightweight convolutional neural network