Efficient Semantic Segmentation Using Spatio-Channel Dilated Convolutions

Kim, Jaeseon; Heo, Yong Seok

doi:10.1109/access.2019.2949076

Cited by 11 publications

(8 citation statements)

References 37 publications

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“…Definition 2: mean intersection over union (MIOU) [13]; calculating the intersection and union ratio to measure the advantages and disadvantages of the algorithm, it is one of the important evaluation indexes in the semantic segmentation model. Here, the intersection and union ratio is the ratio of overlap between the standard labeling of the dataset and the predicted segmentation.…”

Section: Accuracymentioning

confidence: 99%

The Segmentation of Road Scenes Based on Improved ESPNet Model

Jin

Han

et al. 2021

Security and Communication Networks

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Image segmentation is an important research in image processing and machine vision in which automated driving can be seen the main application scene of image segmentation algorithms. Due to the many constraints of power supply and communication in in-vehicle systems, the vast majority of current image segmentation algorithms are implemented based on the deep learning model. Despite the ultrahigh segmentation accuracy, the problem of mesh artifacts and segmentation being too severe is obvious, and the high cost, computational, and power consumption devices required are difficult to apply in real-world scenarios. It is the focus of this paper to construct a road scene segmentation model with simple structure and no need of large computing power under the premise of certain accuracy. In this paper, the ESPNet (Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation) model is introduced in detail. On this basis, an improved ESPNet model is proposed based on ESPNet. Firstly, the network structure of the ESPNet model is optimized, and then, the model is optimized by using a small amount of weakly labeled and unlabeled scene sample data. Finally, the new model is applied to video image segmentation based on dash cam. It is verified on Cityscape, PASCAL VOC 2012, and other datasets that the algorithm proposed in this paper is faster, and the amount of parameters required is less than 1% of other algorithms, so it is suitable for mobile terminals.

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Section: Accuracymentioning

confidence: 99%

The Segmentation of Road Scenes Based on Improved ESPNet Model

Jin

Han

et al. 2021

Security and Communication Networks

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“…It consists of a learning to down-sample module, which is a coarse global feature extractor, feature fusion module, and standard classifier. ESCNet [ 19 ] utilizes an efficient spatio-channel dilated convolution (ESC) module, which is an efficient multi-level dilated convolution module, to accomplish various receptive fields with reduced network parameters and computational complexity. EFSNet [ 42 ] propose the continuous shuffle dilated convolution (CSDC) module for less calculational effort.…”

Section: Related Workmentioning

confidence: 99%

“…Hence, to satisfy the requirements of mobile environments, light but efficient deep-neural network-based methods have been proposed, including ENet [ 15 ], ICNet [ 16 ], ESPNet [ 17 ], ERFNet [ 18 ], and ESCNet [ 19 ]. The above methods can reduce the memory and the number of complexities while presenting accurate performances.…”

Section: Introductionmentioning

confidence: 99%

Knowledge Distillation for Semantic Segmentation Using Channel and Spatial Correlations and Adaptive Cross Entropy

Park

Heo

2020

Sensors

Self Cite

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In this paper, we propose an efficient knowledge distillation method to train light networks using heavy networks for semantic segmentation. Most semantic segmentation networks that exhibit good accuracy are based on computationally expensive networks. These networks are not suitable for mobile applications using vision sensors, because computational resources are limited in these environments. In this view, knowledge distillation, which transfers knowledge from heavy networks acting as teachers to light networks as students, is suitable methodology. Although previous knowledge distillation approaches have been proven to improve the performance of student networks, most methods have some limitations. First, they tend to use only the spatial correlation of feature maps and ignore the relational information of their channels. Second, they can transfer false knowledge when the results of the teacher networks are not perfect. To address these two problems, we propose two loss functions: a channel and spatial correlation (CSC) loss function and an adaptive cross entropy (ACE) loss function. The former computes the full relationship of both the channel and spatial information in the feature map, and the latter adaptively exploits one-hot encodings using the ground truth labels and the probability maps predicted by the teacher network. To evaluate our method, we conduct experiments on scene parsing datasets: Cityscapes and Camvid. Our method presents significantly better performance than previous methods.

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“…We also adopt various experiments with different groups (G), kernel size (K) and staking times (N) to obtain a good trade-off between accuracy performance and time consumption. For a fair comparison, the light-weight segmentation network ESPNet-C [16] and the encoder of the ESCNet [37] are used as baselines to compare parameters, mIoU and inference speed obtained using the Cityscapes validation set.…”

Section: ) Encoder Network With Csdc Modulementioning

confidence: 99%

“…More specifically, EFSNet 1 achieves 54.3 % mIoU with 76.8 k parameters, which is approximately 1.0 % higher than ESPNet. Compared with ESCNet [37], EFSNet 2 achieves 57.2 % mIoU with only 154.2 k parameters with an inference speed of 237.5 FPS.…”

Section: C: Depth Factor (N)mentioning

confidence: 99%

Efficient Fast Semantic Segmentation Using Continuous Shuffle Dilated Convolutions

Wang

2020

IEEE Access

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It is difficult for many semantic segmentation methods to perform useful inferences under extremely resource-constrained devices; therefore, an efficient fast semantic segmentation network (EFSNet) that employs a continuous shuffle dilated convolution (CSDC) and a up-sampling module is proposed in this paper. First, the number of parameters is reduced by group convolutions and the receptive field is enlarged by dilated convolution. Second, a up-sampling module is proposed for reducing noise and increasing inference speed. Finally, with the above CSDC module and up-sampling module, the EFSNet based on a fast semantic segmentation network is obtained. Our experiments on the CamVid and Cityscapes datasets show that the mean intersection over union (mIoU) values obtained by the proposed EFSNet with only 173 k parameters are 61.1 % and 61.9 %, respectively. The method is able to run in real-time at a speed of 332 frames per second (FPS) and 107 FPS on a single NVIDIA Titan Xp GPU. Compared with several existing methods, our network achieves efficient segmentation with low resource consumption.

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Efficient Semantic Segmentation Using Spatio-Channel Dilated Convolutions

Cited by 11 publications

References 37 publications

The Segmentation of Road Scenes Based on Improved ESPNet Model

The Segmentation of Road Scenes Based on Improved ESPNet Model

Knowledge Distillation for Semantic Segmentation Using Channel and Spatial Correlations and Adaptive Cross Entropy

Efficient Fast Semantic Segmentation Using Continuous Shuffle Dilated Convolutions

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