Research on road information extraction from high resolution imagery based on global precedence

Chen, Hao; Yin, Lili; Ma, Li

doi:10.1109/eorsa.2014.6927868

Cited by 8 publications

(5 citation statements)

References 5 publications

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“…The mean Average Precision (mAP) refers to the average of the average precision across all categories and can be used to compare the performance of different algorithms side by side. Generally, the threshold is set to 0.5, that is, the prediction box with an IoU greater than 0.5 is valid and denoted by mAP@0.5, as shown in Equation (19).…”

Section: S-yolov5 Experimental Analysismentioning

confidence: 99%

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A Novel Network Framework on Simultaneous Road Segmentation and Vehicle Detection for UAV Aerial Traffic Images

Xiao,

Min,

Yang

et al. 2024

Sensors

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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 mAP value of the S-YOLOv5 algorithm has an mAP value of 97.40%, which is more than YOLOv5’s 96.95%, which effectively reduces the vehicle omission and false detection rates. By comparison, the results of both algorithms are superior to multiple state-of-the-art methods. The overall framework proposed in this paper has superior performance and is capable of realizing high-quality and high-precision road segmentation and vehicle detection from UAV aerial images.

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Section: S-yolov5 Experimental Analysismentioning

confidence: 99%

“…The traditional methods can be divided into three methods: feature-level [ 10 , 11 , 12 , 13 , 14 , 15 ], object-level [ 16 , 17 , 18 ], and knowledge-level [ 19 , 20 , 21 , 22 , 23 , 24 ] methods. Feature-level methods mainly use the geometric features of road images and radial features.…”

Section: Related Workmentioning

confidence: 99%

A Novel Network Framework on Simultaneous Road Segmentation and Vehicle Detection for UAV Aerial Traffic Images

Xiao,

Min,

Yang

et al. 2024

Sensors

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“…Ghaziani, M. et al [7] utilized the segmentation method, which set several thresholds based on statistical road features, to achieve the binary classification of road and non-road. Hao Chen et al [8] proposed the fusion of prior topological the road data with a road skeleton to obtain high-accuracy road extraction. These methods play a significant role in the performance improvement of road extraction.…”

Section: Introductionmentioning

confidence: 99%

Topological Space Knowledge Distillation for Compact Road Extraction in Optical Remote Sensing Images

et al. 2020

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Road extraction from optical remote sensing images has drawn much attention in recent decades and has a wide range of applications. Most of the previous studies rarely take into account the unique topological characteristics of the road. It is the most apparent feature of linear structure that describes the variety of connection relationships of the road. However, designing a particular topological feature extraction network usually results in a model that is too heavy and impractical. To address the problems mentioned above, in this paper, we propose a lightweight topological space network for road extraction based on knowledge distillation (TSKD-Road). Specifically, (1) narrow and short roads easily influence topological features extracted directly in optical remote sensing images. Therefore, we propose a denser teacher network for extracting road structures; (2) to enhance the weight of topological features, we propose a topological space loss calculation model with multiple widths and depths; (3) based on the above innovations, a topological space knowledge distillation framework is proposed, which aims to transfer different kinds of knowledge acquired in a heavy net to a lightweight net, while significantly improving the lightweight net’s accuracy. Experiments were conducted on two publicly available benchmark datasets, which show the obvious superiority and effectiveness of our network.

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“…(3) Road information extraction methods based on knowledge level: Knowledge-based road extraction methods usually use the previous knowledge about roads, or the supplementary information to extract the targets. Methods such as multi-source data analysis based on existing road databases to guide or assist the extraction of road networks [36,37] are commonly used. They also exploited the self-characteristics of roads, such as spectrum and context [38][39][40][41][42][43][44].…”

Section: Introductionmentioning

confidence: 99%

Aerial Image Road Extraction Based on an Improved Generative Adversarial Network

Zhang

Han

et al. 2019

Remote Sensing

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Aerial photographs and satellite images are one of the resources used for earth observation. In practice, automated detection of roads on aerial images is of significant values for the application such as car navigation, law enforcement, and fire services. In this paper, we present a novel road extraction method from aerial images based on an improved generative adversarial network, which is an end-to-end framework only requiring a few samples for training. Experimental results on the Massachusetts Roads Dataset show that the proposed method provides better performance than several state of the art techniques in terms of detection accuracy, recall, precision and F1-score.

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Research on road information extraction from high resolution imagery based on global precedence

Cited by 8 publications

References 5 publications

A Novel Network Framework on Simultaneous Road Segmentation and Vehicle Detection for UAV Aerial Traffic Images

A Novel Network Framework on Simultaneous Road Segmentation and Vehicle Detection for UAV Aerial Traffic Images

Topological Space Knowledge Distillation for Compact Road Extraction in Optical Remote Sensing Images

Aerial Image Road Extraction Based on an Improved Generative Adversarial Network

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