MFNet: Multi-Feature Fusion Network for Real-Time Semantic Segmentation in Road Scenes

Lu, Mengxu; Chen, Zhenxue; Liu, Chengyun; Ma, Sile; Cai, Lei; Qin, Hao

doi:10.1109/tits.2022.3182311

Cited by 19 publications

(7 citation statements)

References 29 publications

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“…However, its speed is much higher, reaching 131 FPS, a significant advantage compared to the 16.6 FPS of HyperSeg-L. Regarding segmentation speed, the FPS of the algorithm in this paper is lower than DFANet B, 43 GAS, 44 MFNet, 46 and PP-LiteSeg-T. 42 Nevertheless, the mIoU values of this paper's algorithm are 19.9%, 6.4%, 7.7%, and 4.2% higher than these algorithms, respectively, indicating that this algorithm maintains a good balance between accuracy and real-time performance while possessing a certain advantage in terms of accuracy. To visually depict the analysis results, this paper presents a scatter plot of the accuracy-speed comparison of these algorithms on the Camvid test set in Fig.…”

Section: Camvidmentioning

confidence: 76%

“…Table 5 shows a comparison between the proposed algorithm and other excellent real-time semantic segmentation methods on the Camvid test set to validate its effectiveness further. Including deep feature aggregation network (DFANet), 43 MSFNet, 36 graph-guided architecture search for real-time semantic segmentation (GAS), 44 SFNet, 21 temporally distributed network (TDNet), 45 BiSeNetV2, 20 HyperSeg, 40 DDRNet, 15 multi-feature fusion network (MFNet), 46 cascaded selective resolution network (CSRNet), 47 and PP-LiteSeg 42 . As shown in Table 5, the mIoU value of this paper’s algorithm is 79.2%, which is second only to the mIoU value of HyperSeg-L 40 (79.7%).…”

Section: Methodsmentioning

confidence: 99%

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BFBE-Net: deep bilateral fusion and bilateral embedded network for real-time semantic segmentation

Hou,

Cheng,

Dai

et al. 2023

J. Electron. Imag.

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Real-time semantic segmentation has been challenging, and the fusion of features from different branches remains crucial to improvement. The two-branch structure has shown promising results in real-time semantic segmentation. However, upsampling feature maps from the semantic branch to match the detail branch leads to a loss of object feature information and compromises segmentation accuracy. We propose a deep bilateral fusion and bilateral embedded network (BFBE-Net) based on the encoder-decoder structure for real-time semantic segmentation to address these issues. The BFBE-Net adopts a two-branch design in the encoder, with a top-down fusion module and a bottom-up fusion module designed to integrate multi-scale context information in the channel dimension, and assigns different weights to detailed information and semantic information to enhance information characteristics. In the decoder, a bilateral embedded attention module under the guidance of spatial and channel attention integrates semantic and spatial features, gradually upsampling feature maps to reduce the loss of feature information. In addition, an enhanced aggregation pyramid pooling module is designed to efficiently extract contextual information by combining depth-wise asymmetric convolution. The proposed algorithm is evaluated on two benchmark datasets, Cityscapes and CamVid, achieving 78.5% mean intersection over union (mIoU) at 82 frames per second (FPS) on the Cityscapes test set and 79.2% mIoU at 131 FPS on the CamVid test set. The proposed BFBE-Net not only improves segmentation accuracy but also ensures real-time performance.

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Section: Camvidmentioning

confidence: 76%

Section: Methodsmentioning

confidence: 99%

BFBE-Net: deep bilateral fusion and bilateral embedded network for real-time semantic segmentation

Hou,

Cheng,

Dai

et al. 2023

J. Electron. Imag.

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show abstract

“…In addition, various methods such as DDRNet [14], MCINet [15], SFNet [16], SPiN [17], MFNet [18] utilize multiscale and high-resolution features or employ additional modules and branches to provide advantageous guidance for recognizing small objects.…”

Section: Related Workmentioning

confidence: 99%

“…In training, the resolution of the input image is 512 x 512 and batch size 3 are used for training. The proposed RA-Net was designed by extending ERFNet [18] which is known for its lightweight structure. It was implemented using the PyTorch framework on a PC with NVIDIA RTX 3090 GPU.…”

Section: A Experimental Settingsmentioning

confidence: 99%

RoI-Attention Network for Small Disease Segmentation in Crop Images

Moon,

Kim

2024

IEEE Access

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We aim to contribute to deep learning based smart agriculture through semantic segmentation on crop images from real field environment. The key objective is the precise detection of diseases to facilitate the automation of agricultural management. The most significant issue is that the disease regions, serving as Regions of Interest (RoI), are small, making accurate prediction challenging. To address this issue, we propose a new framework of RoI-Attention Network (RA-Net) which additionally utilizes an RoI-attentive image that includes only regions predicted as disease and their surroundings from the input image. Using the RoI-attentive image, RA-Net enhances the representation power for disease regions by guiding the network to re-focus on RoI-associated context based on the initial prediction from the input. Using the proposed RoI-Attention stage, the coarse predictions of disease regions in crop images can be enhanced by incorporating additional sequential RoI-Attention and fusion stages. We have experimentally demonstrated the effectiveness of the proposed RA-Net in predicting small disease regions.

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“…Finally, researchers propose to use parallel structures to capture richer contextual information. To approach this problem, Multi-Feature Fusion Network (MFNet) [19] is proposed, Which adopts parallel attention branch, semantic information acquisition branch and spatial information processing branch to process shallow and deep features. Meanwhile, asymmetric factorized (AF) blocks is used to process shallow features and deep features to obtain local and global information.…”

Section: Introductionmentioning

confidence: 99%

Saliency-Assisted Collaborative Learning Network for Road Scene Semantic Segmentation

2023

KSII TIIS

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Semantic segmentation of road scene is the key technology of autonomous driving, and the improvement of convolutional neural network architecture promotes the improvement of model segmentation performance. The existing convolutional neural network has the simplification of learning knowledge and the complexity of the model. To address this issue, we proposed a road scene semantic segmentation algorithm based on multi-task collaborative learning. Firstly, a depthwise separable convolution atrous spatial pyramid pooling is proposed to reduce model complexity. Secondly, a collaborative learning framework is proposed involved with saliency detection, and the joint loss function is defined using homoscedastic uncertainty to meet the new learning model. Experiments are conducted on the road and nature scenes datasets. The proposed method achieves 70.94% and 64.90% mIoU on Cityscapes and PASCAL VOC 2012 datasets, respectively. Qualitatively, Compared to methods with excellent performance, the method proposed in this paper has significant advantages in the segmentation of fine targets and boundaries.

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MFNet: Multi-Feature Fusion Network for Real-Time Semantic Segmentation in Road Scenes

Cited by 19 publications

References 29 publications

BFBE-Net: deep bilateral fusion and bilateral embedded network for real-time semantic segmentation

BFBE-Net: deep bilateral fusion and bilateral embedded network for real-time semantic segmentation

RoI-Attention Network for Small Disease Segmentation in Crop Images

Saliency-Assisted Collaborative Learning Network for Road Scene Semantic Segmentation

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