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A new optimization approach based on neural architecture search to enhance deep U-Net for efficient road segmentation

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KNOWLEDGE-BASED SYSTEMS 2024年 296卷

作者： Saeedizadeh, Narges Jalali, Seyed Mohammad Jafar Khan, Burhan Kebria, Parham Mohsenzadeh Mohamed, Shady Deakin Univ Inst Intelligent Syst Res & Innovat 75 Pigdons Rd Waurn Ponds Vic 3217 Australia Edith Cowan Univ Sch Sci Joondalup WA 6027 Australia

Neural Architecture Search (NAS) has significantly improved the accuracy of image classification and segmentation. However, these methods concentrate on finding segmentation structures for natural or medical applications. In this study, we introduce a NAS approach based on gradient optimization to identify ideal cell designs for road segmentation. To the best of our knowledge, this work represents the first application of gradient-based NAS to road extraction. Taking insight from the U-Net model and its successful variations in different image segmentation tasks, we propose NAS-enhanced U-Net, illustrated by an equal number of cells in both encoder and decoder levels. While cross-entropy combined with dice loss is commonly used in many segmentation tasks, road extraction brings up a unique challenge due to class imbalance. To address this, we introduce a combination of loss function. This function merges cross-entropy with weighted Dice loss, focusing on elevating the importance of the road class by assigning it a weight (omega), while background Dice values are disregarded. The results indicate that the optimal weight for the proposed model equals 2. Additionally, our work challenges the assumption that increased model parameters or depth inherently leads to improved performance. Therefore, we establish search spaces 2,3,4,5,6,7 and 8 to automatically choose the optimal depth for model. We present promising segmentation results for our proposed method, achieved without any pretraining on the Massachusetts road dataset. Furthermore, these results are compared with those of 14 models categorized into four groups: U-Net, Segnet, FCN8, and Nas-U-Net.

关键词： Neural architecture search U-net, NAS-enhanced U-Net road segmentation Loss function Deep learning

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Research on multitask model of object detection and road segmentation in unstructured road scenes

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MEASUREMENT SCIENCE AND TECHNOLOGY 2024年第6期35卷 065113-065113页

作者： Gao, Chengfei Zhao, Fengkui Zhang, Yong Wan, Maosong Nanjing Forestry Univ Coll Automobile & Traff Engn Nanjing 210037 Peoples R China

With the rapid development of artificial intelligence and computer vision technology, autonomous driving technology has become a hot area of concern. The driving scenarios of autonomous vehicles can be divided into structured scenarios and unstructured scenarios. Compared with structured scenes, unstructured road scenes lack the constraints of lane lines and traffic rules, and the safety awareness of traffic participants is weaker. Therefore, there are new and higher requirements for the environment perception tasks of autonomous vehicles in unstructured road scenes. The current research rarely integrates the target detection and road segmentation to achieve the simultaneous processing of target detection and road segmentation of autonomous vehicle in unstructured road scenes. Aiming at the above issues, a multitask model for object detection and road segmentation in unstructured road scenes is proposed. Through the sharing and fusion of the object detection model and road segmentation model, multitask model can complete the tasks of multi-object detection and road segmentation in unstructured road scenes while inputting a picture. Firstly, MobileNetV2 is used to replace the backbone network of YOLOv5, and multi-scale feature fusion is used to realize the information exchange layer between different features. Subsequently, a road segmentation model was designed based on the DeepLabV3+ algorithm. Its main feature is that it uses MobileNetV2 as the backbone network and combines the binary classification focus loss function for network optimization. Then, we fused the object detection algorithm and road segmentation algorithm based on the shared MobileNetV2 network to obtain a multitask model and trained it on both the public dataset and the self-built dataset NJFU. The training results demonstrate that the multitask model significantly enhances the algorithm's execution speed by approximately 10 frames per scond while maintaining the accuracy of object detection and r

关键词： object detection road segmentation unstructured road scenes multitask model autonomous vehicles

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A Novel Network Framework on Simultaneous road segmentation and Vehicle Detection for UAV Aerial Traffic Images

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SENSORS 2024年第11期24卷 3606页

作者： Xiao, Min Min, Wei Yang, Congmao Song, Yongchao Jiangxi Transportat Investment Grp Co Ltd Project Construct Management Co Nanchang 330108 Peoples R China Yantai Univ Sch Comp & Control Engn Yantai 264005 Peoples R China Zhengzhou Univ Sch Comp & Artificial Intelligence Zhengzhou 450001 Peoples R China

Unmanned Aerial Vehicle (UAV) aerial sensors are an important means of collecting ground image data. Through the road segmentation and vehicle detection of drivable areas in UAV aerial images, they can be applied to monitoring roads, traffic flow detection, traffic management, etc. As well, they can be integrated with intelligent transportation systems to support the related work of transportation departments. Existing algorithms only realize a single task, while intelligent transportation requires the simultaneous processing of multiple tasks, which cannot meet complex practical needs. However, UAV aerial images have the characteristics of variable road scenes, a large number of small targets, and dense vehicles, which make it difficult to complete the tasks. In response to these issues, we propose to implement road segmentation and on-road vehicle detection tasks in the same framework for UAV aerial images, and we conduct experiments on a self-constructed dataset based on the DroneVehicle dataset. For road segmentation, we propose a new algorithm C-DeepLabV3+. The new algorithm introduces the coordinate attention (CA) module, which can obtain more accurate segmentation target location information and make the segmentation target edges more continuous. Also, the improved algorithm introduces the cascade feature fusion module to prevent the loss of detail information in road segmentation and to obtain better segmentation performance. For vehicle detection, we propose an improved algorithm S-YOLOv5 by adding a parameter-free lightweight attention module SimAM. Finally, the proposed road segmentation-vehicle detection framework is utilized to unite the C-DeepLabV3+ and S-YOLOv5 algorithms for the implementation of the serial tasks. The experimental results show that on the constructed ViDroneVehicle dataset, the C-DeepLabV3+ algorithm has an mPA value of 98.75% and an mIoU value of 97.53%, which can better segment the road area and solve the problem of occlusion. The

关键词： UAV aerial traffic images road segmentation vehicle detection attention mechanism cascade feature fusion

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Dual Attention D-LinkNet for road segmentation in Remote Sensing Images 14

Dual Attention D-LinkNet for Road Segmentation in Remote Sen...

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14th IEEE International Conference on Advanced Infocomm Technology (ICAIT)

作者： Wu, Keyu Cai, Feng Fudan Univ Key Lab Informat Sci Electromagnet Waves MoE Shanghai Peoples R China

ISBN: (数字)9781665471565

ISBN: (纸本)9781665471565

road segmentation from high-resolution remote sensing images is a hot research topic in recent years. The roads in high-resolution remote sensing images are irregular with large scale variations. In addition, the scenes of such images are complicated with interference factors that have negative impacts on the segmentation results. On this account, we propose a dual attention network (DA-LinkNet), which combines the advantages of D-LinkNet and the dual attention mechanism. The network is built with D-LinkNet architecture and the Coordinate Attention block is added in its center part, which aims to enhance feature representation. To better combine features from different branches, we adopt the Attentional Feature Fusion block to replace the linear feature fusion operation of skip connection. The comparative experiments on DeepGlobe road Extraction dataset demonstrate the effectiveness and superiority of the proposed method, which exceed state- of-the-art methods by at least 2% for mean Intersection over Union (mIoU).

关键词： remote sensing images road segmentation coordinate attention attentional feature fusion

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Enhancement of the Super Pixel-CNN Based road segmentation Using Cycle Consistent Adversarial Network 14

Enhancement of the Super Pixel-CNN Based Road Segmentation U...

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14th International Conference on Machine Vision (ICMV)

作者： zohourian, Farnoush Pauli, Josef Duisburg Essen Univ Dept Comp Sci & Appl Cognit Sci Chair Intelligent Syst D-47048 Duisburg Germany

ISBN: (数字)9781510650459

ISBN: (纸本)9781510650459;9781510650442

Understanding road scenes is a fundamental problem in advanced driver assistance systems (ADAS) for having a safe and comfortable driving. In our previous work, we proposed a novel approach to utilise the advantages of enhancement-based segmentation method to improve the road segmentation performance at reasonable computational effort. However, unpaved roads, cast shadows, grass and the side walk near the road boundary are still fuzzy and could not precisely to be identified. They suffer from either insufficient training data or the limitation of higher-order potentials in pairwise Conditional Random Field (CRF) models. To overcome these drawbacks, we propose a semi-supervised refinement strategy based on a modified cycle generative adversarial network (CycleGAN), which is more generalisable by enforcing higher-order consistency without being limited to a very specific class of high-order potentials. Proposed method uses only a fraction of annotated images, which may significantly reduce human annotation efforts. Our contribution is that unlike the existing adversarial learning methods, we proposed a modified generative model with fewer parameters than the original CycleGAN, which improves the performance while decreasing the computational cost. Moreover, we enforce cycle consistency to learn the mapping between 4-D channel unpaired images and label domain. To guarantee that the generated image from our modified network corresponds to the original image, we added the distance between a sub-set of images and their paired targeted label. The adversarial learning procedure is limited to the already predicted road boundary obtained from our recent work, which together with the limited number of annotated images boost the segmentation performance. Experiments on KITTI public road segmentation benchmark shows the effectiveness of the 4-7% of improvement with respect to our previous work based on the super pixel-CNN approach and achieves comparable performance among the to

关键词： Super-pixel Semantic segmentation CNN Deep learning Conditional Adversarial Network road segmentation CycleGAN un(semi)-supervised method

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Built-in Depth-Semantic Coupled Encoding for Scene Parsing, Vehicle Detection, and road segmentation

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2021年第9期22卷 5520-5534页

作者： Liu, Shiyu Zhang, Haofeng Shao, Ling Yang, Jingyu Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China Incept Inst Artificial Intelligence IIAI Abu Dhabi U Arab Emirates

Recent representative scene parsing methods based on Convolutional Neural Networks (CNN) have greatly improved spatial resolution of pixel-wise labelling by exploiting multi-scale features and refined boundaries. However, the vast majority of previous works only utilize the color or textural information of images, without considering the depth information, which is beneficial for semantic reasoning. In this paper, we take advantages of the mutual benefit and strong correlation between depth information and semantic information in scene parsing by introducing the Built-in Depth-Semantic Coupled Encoding (BDSCE) module, which adaptively fuses RGB and depth features, and selectively highlights the depth-discriminative features. The proposed BDSCE module is compatible with existing CNN based methods, and can greatly improve scene parsing performance, particularly in those categories that have clear depth distinction and might be misclassified with RGB-only features. Furthermore, we also extend our proposed module to other urban scene semantic reasoning tasks such as vehicle detection and road segmentation, which are implemented by effectively learning and exploiting the encoded depth-semantics and transferring the learned representations with fine-tuning. The extensive experiments on the popular datasets Cityscapes and KITTI demonstrate that our method performs quite well and can significantly improve the state-of-the-art methods.

关键词： Feature extraction Semantics Encoding Vehicle detection roads Correlation Fuses Scene parsing built-in depth-semantic coupled encoding vehicle detection road segmentation

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Fourier-Deformable Convolution Network for road segmentation From Remote Sensing Images

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷

作者： Liu, Huajun Zhou, Xinyu Wang, Cailing Chen, Suting Kong, Hui Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China Nanjing Univ Posts & Telecommun Sch Automat Nanjing 210000 Peoples R China Nanjing Univ Informat Sci & Technol Sch Elect & Informat Engn Nanjing 210044 Peoples R China Univ Macau Dept Comp & Informat Sci CIS Macau Peoples R China

road segmentation from remote sensing images is a challenging task in capturing weak, long, and irregular road features due to the limited connectivity-preserving modeling capability. In this work, we proposed a U-shaped Fourier-deformable convolution network (FDNet) for road segmentation, which integrates the merits of deformable convolutions (DCs) and Fourier convolutions compactly. Specifically, a saliency-aware DC (SD-Conv) layer is proposed for tracing salient road features based on an iterative dynamic offset learning mechanism to grasp extremely tender and weak road objects. Meanwhile, a lightweight global feature extracting module based on spectral convolutions, namely, the adaptive Fourier convolution (AF-Conv) layer, is adopted to learn long-range dependency to extract long and continuous road structures. The proposed SD-Conv layer worked in parallel with the AF-Conv layer to construct a basic and compact block to build the U-shaped FDNet model for road segmentation. Furthermore, to maintain the continuity of road objects in complex road conditions, we introduced a topology-oriented loss function based on the Hausdorff distance (HD) on the persistence diagram (PD) of segmented results, and further combined with softDice loss components for fully supervised training. Our FDNet has been trained and evaluated on two benchmarks, and experimental results show that FDNet achieved state-of-the-art (SOTA) performance. Specifically, it achieved 80.34% on accuracy, 88.42% on precision, and 84.70% on mean intersection over union (mIoU), respectively, on the Massachusetts dataset, and achieved 99.05% on accuracy, 89.21% on precision, 88.61% on recall, and 81.37% on mIoU, respectively, on the DeepGlobe dataset, outperforming most previous methods on both datasets. Codes are available at: https://***/zhoucharming/FDNet.

关键词： Fourier-deformable convolution network (FDNet) remote sensing images road segmentation saliency-aware deformable convolution (SD-Conv)

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Scale Sensitive Neural Network for road segmentation in High-Resolution Remote Sensing Images

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2021年第3期18卷 533-537页

作者： Tan, Xiaowei Xiao, Zhifeng Wan, Qiao Shao, Weiping Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Peoples R China State Grid Zhejiang Elect Power Co Ltd Hangzhou 310007 Peoples R China

road segmentation in remote sensing images has been widely used in many fields. Semantic segmentation, based on deep learning, has become a hot topic for road segmentation. With the deepening of convolutional neural network (CNN) structures, features in the convolution layer that has more semantic information become more important for road segmentation. However, the spatial resolution of the convolutional layer reduced as the CNN network deepens, which causes the extracted roads to lose some important location information. To solve this problem, this letter proposes a novel end-to-end road segmentation method to effectively utilize the different levels of convolutional layers to enhance the model's ability to precisely perceive road edges and shapes. The model includes an encoder and a decoder. The encoder encodes the image to obtain the features of different levels and scales. The decoder consists of two modules: scale fusion module and scale sensitive module. In the scale fusion module, features in pooling layers of different scales are fused to obtain a fusion feature. In a scale sensitive module, a weight tensor at the end of the network is learned to evaluate the importance of fusion features. This road segmentation network has been experimentally verified using public data sets, which greatly improves the road segmentation accuracy and achieves good performance.

关键词： roads Image segmentation Semantics Convolution Spatial resolution Decoding Tensile stress Convolutional neural network~(CNN) multiscale feature remote sensing road segmentation

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roadNet-RT: High Throughput CNN Architecture and SoC Design for Real-Time road segmentation

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2021年第2期68卷 704-714页

作者： Bai, Lin Lyu, Yecheng Huang, Xinming Worcester Polytech Inst Dept Elect & Comp Engn Worcester MA 01609 USA

In recent years, convolutional neural network (CNN) has gained popularity in many engineering applications especially for computer vision. In order to achieve better performance, more complex structures and advanced operations are incorporated into neural networks, which results in very long inference time. For time-critical tasks such as autonomous driving and virtual reality, real-time processing is fundamental. In order to reach real-time processing speed, a lightweight, high-throughput CNN architecture namely roadNet-RT is proposed for road segmentation in this article. It achieves 92.55% MaxF score on KITTI road segmentation dataset. The inference time is about 9 ms per frame when running on GTX 1080 GPU. Comparing to the state-of-the-art network, roadNet-RT speeds up the inference time by a factor of 17.8 at the cost of only 3.75% loss in accuracy. What is more, on CamVid dataset its accuracy is 92.98%. Several techniques such as depthwise separable convolution and non-uniformed kernel size convolution are optimized in the hardware accelerator design. The proposed CNN architecture has been successfully implemented on a ZCU102 MPSoC FPGA that achieves the computation capability of 331 GOPS using INT8 quantization. The system throughput reaches 196.7 frames per second with input image size of 280 x 960 . The source code is published at https://***/linbaiwpi/roadNet-RT.

关键词： road segmentation real-time FPGA neural network

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Remote Sensing Image road segmentation Method Integrating CNN-Transformer and UNet

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IEEE ACCESS 2023年 11卷 144446-144455页

作者： Wang, Rui Cai, Mingxiang Xia, Zixuan Zhou, Zhicui China Transport Telecommun & Informat Ctr Beijing 100011 Peoples R China Heilongjiang Univ Technol Harbin 150022 Heilongjiang Peoples R China No 1 Middle Sch Weifang Jixi 150022 Heilongjiang Peoples R China

Real-time and accurate road information is crucial for updating electronic navigation maps. To address the problem of low precision and poor robustness in current semantic segmentation methods for road extraction from remote sensing imagery, we proposed a UNet road semantic segmentation model based on attention mechanism improvement. First, we introduce a CNN-Transformer hybrid structure to the encoder to enhance the feature extraction capabilities of global and local details. Second, the traditional upsampling module in the decoder is replaced with a dual upsampling module to improve feature extraction capabilities and segmentation accuracy. Furthermore, the hard-swish activation function is used instead of ReLU activation function to smooth the curve, which helps to improve the generalization and non-linear feature extraction abilities and avoid gradient vanishing. Finally, a comprehensive loss function combining cross entropy and dice is used to strengthen the segmentation result constraints and further improve segmentation accuracy. Experimental validation is performed on the Ottawa road Dataset and the Massachusetts road Dataset. Experimental results show that compared with U-Net, PSPNet, DeepLab V3 and TransUNet networks, this algorithm is the best in terms of MIoU, MPA and F1 score. Among them, on the Ottawa road data set, the MPA of this algorithm reached 95.48%. On the Massachusetts road data set, MPA is 92.56%. This method shows good performance in road extraction.

关键词： road segmentation deep learning CNN-transformer attention UNet

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