Adaptive Technique for Contrast Enhancement of Leading Vehicle Tracks

IF 0.8 4区 工程技术 Q3 MULTIDISCIPLINARY SCIENCES
Manoj Kumar Kalra, Ashutosh Trivedi, Sanjay Kumar Shukla
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Abstract

During movement in various unpaved terrain conditions, the track impressions left over by the leading vehicles provide guiding and safe routes in the area. The delineation of these tracks captured by the images can extend immense support for guidance in real time. These tracks that look like edges in coarse-resolution images take the shape of elongated areas in fine-resolution images. In such a scenario, the high pass and edge detection filters give limited information to delineate these tracks passing through different surroundings. However, the distinct texture of these tracks assists in the delineation of these tracks from their surroundings. Gray level co-occurrence matrix (GLCM) representing the spatial relation of pixels is employed here to define the texture. The authors investigated the influence of different resolutions on the distinguishability of these tracks. The study revealed that texture plays an increasing role in distinguishing objects as the image resolution improves. The texture analysis extended to investigate the track impressions left over by the leading vehicle brings out an ample scope in delineating these tracks. The measures could improve the track contrast even better than conventional techniques. To select the most optimal contrast enhancement measure in a given scenario, authors proposed a quantified measure of track index. An investigation is made on the difference-based track index (TI) representing the mean contrast value of the track vis-à-vis off-track areas. The results show an increase in the quantified contrast from 7.83 per cent to 29.06 per cent. The proposed technique highlights the image with the highest track contrast in a given scenario. The study can lead to onboard decision-making for the rut following vehicles moving in low-contrast terrain.
车辆前导轨迹对比度增强自适应技术
在各种未铺设的地形条件下行驶时,领先车辆留下的痕迹为该地区提供了指导和安全路线。图像捕获的这些轨迹的描绘可以为实时制导提供巨大的支持。这些在粗分辨率图像中看起来像边缘的轨迹在精细分辨率图像中呈现出拉长区域的形状。在这种情况下,高通和边缘检测滤波器提供有限的信息来描绘这些轨道通过不同的环境。然而,这些痕迹的独特纹理有助于将这些痕迹与周围环境区分开来。本文采用灰度共生矩阵(GLCM)表示像素的空间关系来定义纹理。研究了不同分辨率对轨道可分辨性的影响。研究表明,随着图像分辨率的提高,纹理在识别物体方面的作用越来越大。将纹理分析扩展到调查领先车辆留下的轨道印痕,为描绘这些轨道提供了充分的范围。这些措施可以比传统技术更好地改善轨道对比度。为了在给定的场景中选择最优的对比度增强措施,作者提出了一种量化的跟踪指数度量。研究了基于差分的轨道指数(TI),该指数表示轨道与-à-vis偏离轨道区域的平均对比度值。结果表明,量化对比度从7.83%增加到29.06%。所提出的技术突出了给定场景中具有最高轨迹对比度的图像。该研究可以为在低对比度地形下行驶的车辆提供车辙跟踪决策。
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来源期刊
Defence Science Journal
Defence Science Journal 综合性期刊-综合性期刊
CiteScore
1.80
自引率
11.10%
发文量
69
审稿时长
7.5 months
期刊介绍: Defence Science Journal is a peer-reviewed, multidisciplinary research journal in the area of defence science and technology. Journal feature recent progresses made in the field of defence/military support system and new findings/breakthroughs, etc. Major subject fields covered include: aeronautics, armaments, combat vehicles and engineering, biomedical sciences, computer sciences, electronics, material sciences, missiles, naval systems, etc.
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