CamVid Dataset Research Articles

Real-Time Semantic Segmentation Algorithm for Street Scenes Based on Attention Mechanism and Feature Fusion

In computer vision, the task of semantic segmentation is crucial for applications such as autonomous driving and intelligent surveillance. However, achieving a balance between real-time performance and segmentation accuracy remains a significant challenge. Although Fast-SCNN is favored for its efficiency and low computational complexity, it still faces difficulties when handling complex street scene images. To address this issue, this paper presents an improved Fast-SCNN, aiming to enhance the accuracy and efficiency of semantic segmentation by incorporating a novel attention mechanism and an enhanced feature extraction module. Firstly, the integrated SimAM (Simple, Parameter-Free Attention Module) increases the network’s sensitivity to critical regions of the image and effectively adjusts the feature space weights across channels. Additionally, the refined pyramid pooling module in the global feature extraction module captures a broader range of contextual information through refined pooling levels. During the feature fusion stage, the introduction of an enhanced DAB (Depthwise Asymmetric Bottleneck) block and SE (Squeeze-and-Excitation) attention optimizes the network’s ability to process multi-scale information. Furthermore, the classifier module is extended by incorporating deeper convolutions and more complex convolutional structures, leading to a further improvement in model performance. These enhancements significantly improve the model’s ability to capture details and overall segmentation performance. Experimental results demonstrate that the proposed method excels in processing complex street scene images, achieving a mean Intersection over Union (mIoU) of 71.7% and 69.4% on the Cityscapes and CamVid datasets, respectively, while maintaining inference speeds of 81.4 fps and 113.6 fps. These results indicate that the proposed model effectively improves segmentation quality in complex street scenes while ensuring real-time processing capabilities.

Containment Control-Guided Boundary Information for Semantic Segmentation

Real-time semantic segmentation is a challenging task in computer vision, especially in complex scenes. In this study, a novel three-branch semantic segmentation model is designed, aiming to effectively use boundary information to improve the accuracy of semantic segmentation. The proposed model introduces the concept of containment control in a pioneering way, which treats image interior elements as well as image boundary elements as followers and leaders in containment control, respectively. Based on this, we utilize two learnable feature fusion matrices in the high-level semantic information stage of the model to quantify the fusion process of internal and boundary features. Further, we design a dedicated loss function to update the parameters of the feature fusion matrices based on the criterion of containment control, which enables fine-grained communication between target features. In addition, our model incorporates a Feature Enhancement Unit (FEU) to tackle the challenge of maximizing the utility of multi-scale features essential for semantic segmentation tasks through the meticulous reconstruction of these features. The proposed model proves effective on the publicly available Cityscapes and CamVid datasets, achieving a trade-off between effectiveness and speed.

BMSeNet: Multiscale Context Pyramid Pooling and Spatial Detail Enhancement Network for Real-Time Semantic Segmentation.

Most real-time semantic segmentation networks use shallow architectures to achieve fast inference speeds. This approach, however, limits a network's receptive field. Concurrently, feature information extraction is restricted to a single scale, which reduces the network's ability to generalize and maintain robustness. Furthermore, loss of image spatial details negatively impacts segmentation accuracy. To address these limitations, this paper proposes a Multiscale Context Pyramid Pooling and Spatial Detail Enhancement Network (BMSeNet). First, to address the limitation of singular semantic feature scales, a Multiscale Context Pyramid Pooling Module (MSCPPM) is introduced. By leveraging various pooling operations, this module efficiently enlarges the receptive field and better aggregates multiscale contextual information. Moreover, a Spatial Detail Enhancement Module (SDEM) is designed, to effectively compensate for lost spatial detail information and significantly enhance the perception of spatial details. Finally, a Bilateral Attention Fusion Module (BAFM) is proposed. This module leverages pixel positional correlations to guide the network in assigning appropriate weights to the features extracted from the two branches, effectively merging the feature information of both branches. Extensive experiments were conducted on the Cityscapes and CamVid datasets. Experimental results show that the proposed BMSeNet achieves a good balance between inference speed and segmentation accuracy, outperforming some state-of-the-art real-time semantic segmentation methods.

P2AT: Pyramid pooling axial transformer for real-time semantic segmentation

Recently, Transformer-based models have achieved promising results in various vision tasks, due to their ability to model long-range dependencies. However, transformers are computationally expensive, which limits their applications in real-time tasks such as autonomous driving. In addition, efficient local and global feature selection and fusion are vital for accurate dense prediction, especially driving scene understanding tasks. In this paper, we propose a real-time semantic segmentation architecture named Pyramid Pooling Axial Transformer (P2AT). The proposed P2AT takes a coarse feature from the CNN encoder to produce scale-aware contextual features, which are then combined with the multi-level feature aggregation scheme to produce enhanced contextual features. Specifically, we introduce a pyramid pooling axial transformer to capture intricate spatial and channel dependencies, leading to improved performance on semantic segmentation. Then, we design a Bidirectional Fusion module (BiF) to combine semantic information at different levels. Meanwhile, a Global Context Enhancer (GCE) is introduced to compensate for the inadequacy of concatenating different semantic levels. Finally, a decoder block is proposed to help maintain a larger receptive field. We evaluate P2AT variants on three challenging scene-understanding datasets. In particular, our P2AT variants achieve state-of-art results on the Camvid dataset 80.5%, 81.0%, 81.1% for P2AT-S, P2AT-M, and P2AT-L, respectively. Furthermore, our experiments on Cityscapes and Pascal VOC 2012 have demonstrated the efficiency of the proposed architecture. The source code will be available at https://github.com/mohamedac29/P2AT.

LACTNet: A Lightweight Real-Time Semantic Segmentation Network Based on an Aggregated Convolutional Neural Network and Transformer

Transformers have demonstrated a significant advantage over CNNs in modeling long-range dependencies, leading to increasing attention being paid towards their application in semantic segmentation tasks. In the present work, a novel semantic segmentation model, LACTNet, is introduced, which synergistically combines Transformer and CNN architectures for the real-time processing of local and global contextual features. LACTNet is designed with a lightweight Transformer, which integrates a specially designed gated convolutional feedforward network, to establish feature dependencies across distant regions. A Lightweight Average Feature Bottleneck (LAFB) module is designed to effectively capture spatial detail information within the features, thereby enhancing segmentation accuracy. To address the issue of spatial feature loss in the decoder, a long skip-connection approach is employed through the designed Feature Fusion Enhancement Module (FFEM), which enhances the integrity of spatial features and the feature interaction capability in the decoder. LACTNet is evaluated on two datasets, achieving a segmentation accuracy of 74.8% mIoU and a frame rate of 90 FPS on the Cityscapes dataset, and a segmentation accuracy of 71.8% mIoU with a frame rate of 126 FPS on the CamVid dataset.

Multi-scale full spike pattern for semantic segmentation

Spiking neural networks (SNNs), as the brain-inspired neural networks, encode information in spatio-temporal dynamics. They have the potential to serve as low-power alternatives to artificial neural networks (ANNs) due to their sparse and event-driven nature. However, existing SNN-based models for pixel-level semantic segmentation tasks suffer from poor performance and high memory overhead, failing to fully exploit the computational effectiveness and efficiency of SNNs. To address these challenges, we propose the multi-scale and full spike segmentation network (MFS-Seg), which is based on the deep direct trained SNN and represents the first attempt to train a deep SNN with surrogate gradients for semantic segmentation. Specifically, we design an efficient fully-spike residual block (EFS-Res) to alleviate representation issues caused by spiking noise on different channels. EFS-Res utilizes depthwise separable convolution to improve the distributions of spiking feature maps. The visualization shows that our model can effectively extract the edge features of segmented objects. Furthermore, it can significantly reduce the memory overhead and energy consumption of the network. In addition, we theoretically analyze and prove that EFS-Res can avoid the degradation problem based on block dynamical isometry theory. Experimental results on the Camvid dataset, the DDD17 dataset, and the DSEC-Semantic dataset show that our model achieves comparable performance to the mainstream UNet network with up to 31× fewer parameters, while significantly reducing power consumption by over 13×. Overall, our MFS-Seg model demonstrates promising results in terms of performance, memory efficiency, and energy consumption, showcasing the potential of deep SNNs for semantic segmentation tasks. Our code is available in https://github.com/BICLab/MFS-Seg.

Fast-DSAGCN: Enhancing semantic segmentation with multifaceted attention mechanisms

Real-time semantic segmentation provides precise insights into dynamic street environments for autonomous driving, traffic control, and urban planning. However, state-of-the-art models following attention mechanisms and deep convolutional neural networks have improved semantic segmentation at the cost of complex architectures and high computation complexity. The study aims to mitigate the presence of gridding artifacts and enhance semantic segmentation performance. In addition, we propose a multi-level downsampling approach before employing the depth-wise split separable global convolution with the bottleneck to achieve a trade-off between accuracy and inference time. The spatial attention module used in this study effectively keeps low-level spatial characteristics, enhancing the accuracy of localization, robustness against disturbances, processing efficiency, and the ability to handle occlusions. Thorough tests of the Cityscapes and CamVid datasets available for public access indicate that the model presented is capable of efficiently processing high-resolution photos in real time, resulting in exceptional performance. The model has achieved an accuracy of 72.3% on the cityscapes dataset and 72.7% on the CamVid dataset.

SasWOT: Real-Time Semantic Segmentation Architecture Search WithOut Training

In this paper, we present SasWOT, the first training-free Semantic segmentation Architecture Search (SAS) framework via an auto-discovery proxy. Semantic segmentation is widely used in many real-time applications. For fast inference and memory efficiency, Previous SAS seeks the optimal segmenter by differentiable or RL Search. However, the significant computational costs of these training-based SAS limit their practical usage. To improve the search efficiency, we explore the training-free route but empirically observe that the existing zero-cost proxies designed on the classification task are sub-optimal on the segmentation benchmark. To address this challenge, we develop a customized proxy search framework for SAS tasks to augment its predictive capabilities. Specifically, we design the proxy search space based on the some observations: (1) different inputs of segmenter statistics can be well combined; (2) some basic operators can effectively improve the correlation. Thus, we build computational graphs with multiple statistics as inputs and different advanced basis arithmetic as the primary operations to represent candidate proxies. Then, we employ an evolutionary algorithm to crossover and mutate the superior candidates in the population based on correlation evaluation. Finally, based on the searched proxy, we perform the segmenter search without candidate training. In this way, SasWOT not only enables automated proxy optimization for SAS tasks but also achieves significant search acceleration before the retrain stage. Extensive experiments on Cityscapes and CamVid datasets demonstrate that SasWOT achieves superior trade-off between accuracy and speed over several state-of-the-art techniques. More remarkably, on Cityscapes dataset, SasWOT achieves the performance of 71.3% mIoU with the speed of 162 FPS.

MCFNet: Multi-Attentional Class Feature Augmentation Network for Real-Time Scene Parsing

For real-time scene parsing tasks, capturing multi-scale semantic features and performing effective feature fusion is crucial. However, many existing solutions ignore stripe-shaped things like poles, traffic lights and are so computationally expensive that cannot meet the high real-time requirements. This article presents a novel model, the Multi-Attention Class Feature Augmentation Network (MCFNet) to address this challenge. MCFNet is designed to capture long-range dependencies across different scales with low computational cost and to perform a weighted fusion of feature maps. It features the BAM (Strip Matrix Based Attention Module) for extracting strip objects in images. The BAM module replaces the conventional self-attention method using square matrices with strip matrices, which allows it to focus more on strip objects while reducing computation. Additionally, MCFNet has a parallel branch that focuses on global information based on self-attention to avoid wasting computation. The two branches are merged to enhance the performance of traditional self-attention modules. Experimental results on two mainstream datasets demonstrate the effectiveness of MCFNet. On the Camvid and Cityscapes test sets, MCFNet achieved 207.5 FPS/73.5% mIoU and 136.1 FPS/71.63% mIoU, respectively. The experiments show that MCFNet outperforms other models on the Camvid dataset and can significantly improve the performance of real-time scene parsing tasks.

Improving Semantic Segmentation via Efficient Self-Training.

Starting from the seminal work of Fully Convolutional Networks (FCN), there has been significant progress on semantic segmentation. However, deep learning models often require large amounts of pixelwise annotations to train accurate and robust models. Given the prohibitively expensive annotation cost of segmentation masks, we introduce a self-training framework in this paper to leverage pseudo labels generated from unlabeled data. In order to handle the data imbalance problem of semantic segmentation, we propose a centroid sampling strategy to uniformly select training samples from every class within each epoch. We also introduce a fast training schedule to alleviate the computational burden. This enables us to explore the usage of large amounts of pseudo labels. Our Centroid Sampling based Self-Training framework (CSST) achieves state-of-the-art results on Cityscapes and CamVid datasets. On PASCAL VOC 2012 test set, our models trained with the original train set even outperform the same models trained on the much bigger augmented train set. This indicates the effectiveness of CSST when there are fewer annotations. We also demonstrate promising few-shot generalization capability from Cityscapes to BDD100K and from Cityscapes to Mapillary datasets.

A new CNN-based semantic object segmentation for autonomous vehicles in urban traffic scenes

Semantic segmentation is the most important stage of making sense of the visual traffic scene for autonomous driving. In recent years, convolutional neural networks (CNN)-based methods for semantic segmentation of urban traffic scenes are among the trending studies. However, the methods developed in the studies carried out so far are insufficient in terms of accuracy performance criteria. In this study, a new CNN-based semantic segmentation method with higher accuracy performance is proposed. A new module, the Attentional Atrous Feature Pooling (AAFP) Module, has been developed for the proposed method. This module is located between the encoder and decoder in the general network structure and aims to obtain multi-scale information and add attentional features to large and small objects. As a result of experimental tests with the CamVid data set, an accuracy value of approximately 2% higher was achieved with a mIoU value of 70.59% compared to other state-of-art methods. Therefore, the proposed method can semantically segment objects in the urban traffic scene better than other methods.

Attention based lightweight asymmetric network for real-time semantic segmentation

Real-time semantic segmentation is one of the important tasks in the field of computer vision, which is widely used in the fields of autonomous driving and medical imaging. Existing lightweight networks usually improve inference speed at the sacrifice of segmentation accuracy. How to achieve a balance between accuracy and speed is still a challenging problem for real-time semantic segmentation. In this paper, we propose an attention based lightweight asymmetric network (ALANet) to address this problem. Specifically, in the encoder, a channel-wise attention based depth-wise asymmetric block (CADAB) is designed to extract sufficient features, which has a small number of parameters. In the decoder, a spatial attention based pyramid pooling (SAPP) module is presented to aggregate multi-scale context information by using a few convolutions and poolings; and a pixel-wise attention based multi-scale feature fusion (PAMFF) module is developed to fuse features from different scales and generate pixel-wise attention for improving image restoration. Our ALANet has only 1.32M parameters. Experimental results on the Cityscapes and CamVid datasets show that ALANet obtains the segmentation accuracy (mIoU) of 74.4% and 69.5% and the inference speed of 115.6FPS and 113.2FPS, respectively. These results demonstrate that ALANet achieves a good balance between accuracy and speed.

Video Semantic Segmentation Network with Low Latency Based on Deep Learning

Recently, new advances in deep learning algorithms have yielded some fascinating results in the field of computer vision technology. As a result, it can now perform activities that formerly required the use of human vision and the brain. Classification, object identification, and semantic segmentation have all seen substantial advancements in deep learning architecture in the last few years. For still images and movies, there has been a major advancement in the field of semantic segmentation. In practical uses like autonomous vehicles, segmenting semantic video continues to be difficult due to high-performance standards, the high cost of convolutional neural networks (CNNs), and the significant need for low latency. An effective machine-learning environment will be developed to meet the performance and latency challenges outlined above. The use of deep learning architectures like SegNet and FlowNet2.0 on the CamVid dataset enables this environment to conduct pixel-wise semantic segmentation of video properties while maintaining low latency. As a result, it is ideally suited for real-world applications since it takes advantage of both SegNet and FlowNet topologies. The decision network determines whether an image frame should be processed by a segmentation network or an optical flow network based on the expected confidence score. In conjunction with adaptive scheduling of the key frame approach, this technique for decision-making can help to speed up the procedure. Using the ResNet50 SegNet model, a mean Intersection on Union (IoU) of "54.27 percent" and an average frame per second of "19.57" were observed. Aside from decision network and adaptive key frame sequencing, it was discovered that FlowNet2.0 increased the frames processed per second9(fps) to "30.19" on GPU with a mean IoU of "47.65%". Because the GPU was utilized "47.65%" of the time, this resulted. There has been an increase in the speed of the Video semantic segmentation network without sacrificing quality, as demonstrated by this improvement in performance.

Vehicle Detection and Speed Estimation Using Semantic Segmentation with Low Latency

Computer vision researchers are actively studying the use of video in traffic monitoring. TrafficMonitor uses a stationary calibrated camera to automatically track and classify vehicles on roadways. In practical uses like autonomous vehicles, segmenting semantic video continues to be difficult due to high-performance standards, the high cost of convolutional neural networks (CNNs), and the significant need for low latency. An effective machine learning environment will be developed to meet the performance and latency challenges outlined above. The use of deep learning architectures like SegNet and Flownet2.0 on the CamVid dataset enables this environment to conduct pixel-wise semantic segmentation of video properties while maintaining low latency. In this work, we discuss some state-of-the-art ways to estimating the speed of vehicles, locating vehicles, and tracking objects. As a result, it is ideally suited for real-world applications since it takes advantage of both SegNet and Flownet topologies. The decision network determines whether an image frame should be processed by a segmentation network or an optical flow network based on the expected confidence score. In conjunction with adaptive scheduling of the key frame approach, this technique for decision-making can help to speed up the procedure. Using the ResNet50 SegNet model, a mean IoU of "54.27 per cent" and an average fps of "19.57" were observed. Aside from decision network and adaptive key frame sequencing, it was discovered that FlowNet2.0 increased the frames processed per second to "30.19" on GPU with such a mean IoU of "47.65%". Because the GPU was utilised "47.65%" of the time, this resulted. There has been an increase in the speed of the Video semantic segmentation network without sacrificing quality, as demonstrated by this improvement in performance.

Lightweight semantic segmentation network with configurable context and small object attention.

The current semantic segmentation algorithms suffer from encoding feature distortion and small object feature loss. Context information exchange can effectively address the feature distortion problem, but it has the issue of fixed spatial range. Maintaining the input feature resolution can reduce the loss of small object information but would slow down the network's operation speed. To tackle these problems, we propose a lightweight semantic segmentation network with configurable context and small object attention (CCSONet). CCSONet includes a long-short distance configurable context feature enhancement module (LSCFEM) and a small object attention decoding module (SOADM). The LSCFEM differs from the regular context exchange module by configuring long and short-range relevant features for the current feature, providing a broader and more flexible spatial range. The SOADM enhances the features of small objects by establishing correlations among objects of the same category, avoiding the introduction of redundancy issues caused by high-resolution features. On the Cityscapes and Camvid datasets, our network achieves the accuracy of 76.9 mIoU and 73.1 mIoU, respectively, while maintaining speeds of 87 FPS and 138 FPS. It outperforms other lightweight semantic segmentation algorithms in terms of accuracy.

Lightweight multi-scale attention-guided network for real-time semantic segmentation

The wide application of small mobile devices makes the demand for lightweight real-time semantic segmentation algorithm become more and more intense, which makes it become one of the most popular research topics in the field of computer vision. However, some current methods blindly pursue low parameter numbers and high inference speeds, resulting in excessively low model accuracy and a loss of practical value. Therefore, a lightweight multi-scale attention-guided network for real-time semantic segmentation(LMANet) based on asymmetric encoder-decoder is proposed in this paper to solve the above dilemmas. In the encoder, we propose multi-scale asymmetric residual(MAR) modules to extract local spatial information and context information to enhance feature expression. In the decoder, we design an attention feature fusion(AFF) module and an attention pyramid refining (APR) module. AFF module guides the fusion of low-level and middle-level feature information through high-level semantic information, and finally refines the fusion result through APR module. In addition, we improve the segmentation performance of the model with the help of the attention modules in the network. Our network is tested on two complex urban road datasets. The experimental results show that LMANet achieves 70.6% mIoU and 66.5% mIoU on Cityscapes and Camvid datasets at 112FPS and 333FPS respectively, only 0.95 M parameters without any pre-training or pre-processing. Compared with most of existing state-of-the-art models, our network not only guarantees reasonable inference speed and parameter quantity, but also improves the accuracy as much as possible, which makes it more practical.

Block attention network: A lightweight deep network for real-time semantic segmentation of road scenes in resource-constrained devices

Deep-learning-based semantic segmentation networks typically incorporate object classification networks in their backbone. This leads to a loss of context because classification networks have a smaller field of view. The architecture has been extended to recover context with additional downsampling feature maps, a parallel context branch, or pyramid pooling modules after the backbone. However, these extensions increase multiply–accumulate operations and memory requirements, thus, making them unsuitable for resource-constrained devices. To overcome this limitation, a novel convolutional building block with attention-based context guidance is proposed. The block is repeated to build an efficient encoder–decoder network. Our network runs in real-time, has a lightweight design with only 0.72 Million parameters, and achieves 70.1%, and 66.3% mean intersection-over-union scores on the highly competitive Cityscapes and CamVid datasets, respectively. An efficient decoder is also designed to replace other semantic segmentation network decoders with minimal performance loss. The performance measures on mobile platforms show that our network suits resource-constrained devices. Further, experimental results show that the proposed method can optimally balance the model size-inference speed and segmentation accuracy.

Based on cross-scale fusion attention mechanism network for semantic segmentation for street scenes.

Semantic segmentation, which is a fundamental task in computer vision. Every pixel will have a specific semantic class assigned to it through semantic segmentation methods. Embedded systems and mobile devices are difficult to deploy high-accuracy segmentation algorithms. Despite the rapid development of semantic segmentation, the balance between speed and accuracy must be improved. As a solution to the above problems, we created a cross-scale fusion attention mechanism network called CFANet, which fuses feature maps from different scales. We first design a novel efficient residual module (ERM), which applies both dilation convolution and factorized convolution. Our CFANet is mainly constructed from ERM. Subsequently, we designed a new multi-branch channel attention mechanism (MCAM) to refine the feature maps at different levels. Experiment results show that CFANet achieved 70.6% mean intersection over union (mIoU) and 67.7% mIoU on Cityscapes and CamVid datasets, respectively, with inference speeds of 118 FPS and 105 FPS on NVIDIA RTX2080Ti GPU cards with 0.84M parameters.

LBARNet: Lightweight bilateral asymmetric residual network for real-time semantic segmentation

Real-time semantic segmentation, as a key technique for scene understanding, has been an important research topic in the field of computer vision in recent years. However, existing models are unable to achieve good segmentation accuracy on mobile devices due to their huge computational overhead, which makes it difficult to meet actual industrial requirements. To address the problems faced by current semantic segmentation tasks, this paper proposes a lightweight bilateral asymmetric residual network (LBARNet) for real-time semantic segmentation. First, we propose the bilateral asymmetric residual (BAR) module. This module learns multi-scale feature representations with strong semantic information at different stages of the semantic information extraction branch, thus improving pixel classification performance. Secondly, the spatial information extraction (SIE) module is constructed in the spatial detail extraction branch to capture multi-level local features of the shallow network to compensate for the lost geometric information in the downsampling stage. At the same time, we design the attention mechanism perception (AMP) module in the jump connection part to enhance the contextual representation. Finally, we design the dual branch feature fusion (DBF) module to exploit the correspondence between higher-order features and lower-order features to fuse spatial and semantic information appropriately. The experimental results show that LBARNet, without any pre-training and pre-processing and using only 0.6M parameters, achieves 70.8% mloU and 67.2% mloU on the Cityscapes dataset and Camvid dataset, respectively. LBARNet maintain a high segmentation accuracy while using a smaller number of parameters compared to most existing state-of-the-art models.

MFAFNet: A Lightweight and Efficient Network with Multi-Level Feature Adaptive Fusion for Real-Time Semantic Segmentation.

Currently, real-time semantic segmentation networks are intensely demanded in resource-constrained practical applications, such as mobile devices, drones and autonomous driving systems. However, most of the current popular approaches have difficulty in obtaining sufficiently large receptive fields, and they sacrifice low-level details to improve inference speed, leading to decreased segmentation accuracy. In this paper, a lightweight and efficient multi-level feature adaptive fusion network (MFAFNet) is proposed to address this problem. Specifically, we design a separable asymmetric reinforcement non-bottleneck module, which designs a parallel structure to extract short- and long-range contextual information and use optimized convolution to increase the inference speed. In addition, we propose a feature adaptive fusion module that effectively balances feature maps with multiple resolutions to reduce the loss of spatial detail information. We evaluate our model with state-of-the-art real-time semantic segmentation methods on the Cityscapes and Camvid datasets. Without any pre-training and post-processing, our MFAFNet has only 1.27 M parameters, while achieving accuracies of 75.9% and 69.9% mean IoU with speeds of 60.1 and 82.6 FPS on the Cityscapes and Camvid test sets, respectively. The experimental results demonstrate that the proposed method achieves an excellent trade-off between inference speed, segmentation accuracy and model size.