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DroneRTEF:development of a novel adaptive framework for railroad track extraction in drone images
Pattern Analysis and Applications ( IF 3.9 ) Pub Date : 2021-06-29 , DOI: 10.1007/s10044-021-00994-w
Aradhya Saini , Dharmendra Singh

Railroad track health monitoring is a challenging yet important task as it affects the safety of railroad systems. Railroad track extraction presents an immediate advantage during railroad inspections in an efficient and cost-effective manner. At present, human inspectors, inspection trains, and rail-mounted vehicles equipped with cameras are prevalent image acquisition systems(IAS) in the track extraction module. However, these IAS face various challenges such as high operability cost, railroad closed for normal traffic, and inaccessibility to certain geographical locations. In such scenarios, drones act as effective IAS. Therefore, this paper presents a novel and adaptive railroad track extraction framework for drone images (DI) captured under uneven illumination, at different drone flight heights, with varying rail line orientations, and in complex railroad environments. We termed this framework as DroneRTEF. This work primarily focuses on two aspects of drone-based railroad track images: image enhancement and image analysis. With regard to the first aspect, a global image enhancement algorithm named adaptive colour space-based masking (ACSM) is developed to enhance railroad track images and identify rail lines. The rail lines and background can be highlighted and homogenized, respectively, in DI captured under various sunlight intensity using ACSM due to its illuminance independence. With regard to the second aspect, the Hough parameter space analysis-based novel Hough transform-ground sample distance(HT-GSD) method is presented in this paper. The proposed HT-GSD method emphasizes on rail line detections at varying line orientations and different flight heights. The track extraction is then performed by a coordinate transformation technique. The approach has been successfully tested and validated on various DI. The efficacy of our framework for rail line detection is identified by comparing it with other line detection model. Performances of these methods are tested using metrics such as precision, recall and accuracies of the detections. Results obtained show that our method is superior to another model. Therefore, DroneRTEF is an efficient and feasible method for railroad track extraction in DI.



中文翻译:

DroneRTEF:开发一种用于无人机图像中铁路轨道提取的新型自适应框架

铁路轨道健康监测是一项具有挑战性但又很重要的任务,因为它会影响铁路系统的安全。铁路轨道提取在铁路检查期间以高效且具有成本效益的方式呈现出直接优势。目前,人类检查员、检查列车和装有摄像头的轨道车辆是轨道提取模块中流行的图像采集系统(IAS)。然而,这些 IAS 面临着各种挑战,例如高运营成本、铁路因正常交通而关闭以及无法到达某些地理位置。在这种情况下,无人机充当有效的 IAS。因此,本文提出了一种新颖的自适应铁路轨道提取框架,用于在不同的无人机飞行高度、不同的铁路线方向、不均匀光照下捕获的无人机图像(DI),以及复杂的铁路环境。我们将此框架称为 DroneRTEF。这项工作主要关注基于无人机的铁路轨道图像的两个方面:图像增强和图像分析。针对第一方面,开发了一种名为自适应颜色空间掩蔽(ACSM)的全局图像增强算法,以增强铁路轨道图像和识别铁路线。由于其照度独立性,使用 ACSM 在各种阳光强度下捕获的 DI 中可以分别突出显示和均匀化铁路线和背景。针对第二个方面,本文提出了一种基于霍夫参数空间分析的新型霍夫变换-地面样本距离(HT-GSD)方法。所提出的 HT-GSD 方法强调在不同线路方向和不同飞行高度下的铁路线检测。然后通过坐标变换技术执行轨迹提取。该方法已在各种 DI 上成功测试和验证。通过将其与其他线路检测模型进行比较,可以确定我们的铁路线路检测框架的有效性。这些方法的性能使用检测的精确度、召回率和准确度等指标进行测试。获得的结果表明我们的方法优于另一个模型。因此,DroneRTEF 是 DI 中铁路轨道提取的一种有效且可行的方法。这些方法的性能使用检测的精确度、召回率和准确度等指标进行测试。获得的结果表明我们的方法优于另一个模型。因此,DroneRTEF 是 DI 中铁路轨道提取的一种有效且可行的方法。这些方法的性能使用检测的精确度、召回率和准确度等指标进行测试。获得的结果表明我们的方法优于另一个模型。因此,DroneRTEF 是 DI 中铁路轨道提取的一种有效且可行的方法。

更新日期:2021-06-29
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