Online Road Detection under a Shadowy Traffic Image Using a Learning-Based Illumination-Independent Image

The order/disorder characteristics of a compartment fire are researched based on experimental data. From our analysis performed by new, pioneering methods, we claim that the parametric Jensen-Shannon complexity can be successfully used to detect unusual data, and that one can use it also as a means to perform relevant analysis of fire experiments. Thoroughly comparing the performance of different algorithms (known as permutation entropy and two-length permutation entropy) to extract the probability distribution is an essential step. We discuss some of the theoretical assumptions behind each step and stress that the role of the parameter is to fine-tune the results of the Jensen-Shannon statistical complexity. Note that the Jensen-Shannon statistical complexity is symmetric, while its parametric version displays a symmetric duality due to the a priori probabilities used.Symmetry 2020, 12, 22 2 of 12 which were fixed at key locations (see Figure 1) and connected to a data acquisition logger. Flames were observed to impinge on the ceiling and exit through the opening, and we also noted the ignition of crumpled newspaper and stages of fire development that are known as indicators of flashover.Symmetry 2020, 12, x FOR PEER REVIEW 2 of 12

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“…To understand this material the reader is referred to [20]. For the use of the permutation entropy in another framework see [21,22].…”

Section: Concluding Remarks On the Limitations Of Our Studymentioning

confidence: 99%

Parametric Jensen-Shannon Statistical Complexity and Its Applications on Full-Scale Compartment Fire Data

2019

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“…Road extraction based on high-resolution remote sensing images is an important task in the remote sensing image processing community, and ensuring the connectivity and integrity of the extracted roads is of great importance for many applications, such as urban construction, transportation planning, road network updating, and route navigation [1][2][3]. However, the connectivity and integrity of the extracted roads can hardly be ensured due to the following asymmetry questions regarding roads in high-resolution remote sensing images.…”

Section: Introductionmentioning

confidence: 99%

Road Extraction Method of Remote Sensing Image Based on Deformable Attention Transformer

Zhao,

Zhang,

Meng

et al. 2024

Symmetry

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Road extraction is a typical task in the semantic segmentation of remote sensing images, and one of the most efficient techniques for solving this task in recent years is the vision transformer technique. However, roads typically exhibit features such as uneven scales and low signal-to-noise ratios, which can be understood as the asymmetry between the road and the background category and the asymmetry in the transverse and longitudinal shape of the road. Existing vision transformer models, due to their fixed sliding window mechanism, cannot adapt to the uneven scale issue of roads. Additionally, self-attention, based on fully connected mechanisms for long sequences, may suffer from attention deviation due to excessive noise, making it unsuitable for low signal-to-noise ratio scenarios in road segmentation, resulting in incomplete and fragmented road segmentation results. In this paper, we propose a road extraction based on deformable self-attention computation, termed DOCswin-Trans (Deformable and Overlapped Cross-Window Transformer), to solve these problems. On the one hand, we develop a DOC-Transformer block to address the scale imbalance issue, which can utilize the overlapped window strategy to preserve the overall contextual semantic information of roads as much as possible. On the other hand, we propose a deformable window strategy to adaptively resample input vectors, which can direct attention automatically to the foreground areas relevant to roads and thereby address the low signal-to-noise ratio problem. We evaluate the proposed method on two popular road extraction datasets (i.e., DeepGlobe and Massachusetts datasets). The experimental results demonstrate that the proposed method outperforms baseline methods. On the DeepGlobe dataset, the proposed method achieves an IoU improvement ranging from 0.63% to 5.01% compared to baseline methods. On the Massachusetts dataset, our method achieves an IoU improvement ranging from 0.50% to 6.24% compared to baseline methods.

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“…Vehicle and pedestrian detection and classification on the road are one of the challenges of advanced driver assistance systems (ADAS) and are essential for traffic safety applications. Based on this, road detection [4], vehicle detection [5,6], and pedestrian detection [7] are the key steps to realize vehicle autonomous driving technology.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection and Classification of Road, Car, and Pedestrian Using Binocular Cameras in Traffic Scenes with a Common Framework

Song

Yao

et al. 2020

Complexity

Self Cite

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In order to solve the problems of traffic object detection, fuzzification, and simplification in real traffic environment, an automatic detection and classification algorithm for roads, vehicles, and pedestrians with multiple traffic objects under the same framework is proposed. We construct the final V view through a considerate U-V view method, which determines the location of the horizon and the initial contour of the road. Road detection results are obtained through error label reclassification, omitting point reassignment, and so an. We propose a peripheral envelope algorithm to determine sources of vehicles and pedestrians on the road. The initial segmentation results are determined by the regional growth of the source point through the minimum neighbor similarity algorithm. Vehicle detection results on the road are confirmed by combining disparity and color energy minimum algorithms with the object window aspect ratio threshold method. A method of multifeature fusion is presented to obtain the pedestrian target area, and the pedestrian detection results on the road are accurately segmented by combining the disparity neighbor similarity and the minimum energy algorithm. The algorithm is tested in three datasets of Enpeda, KITTI, and Daimler; then, the corresponding results prove the efficiency and accuracy of the proposed approach. Meanwhile, the real-time analysis of the algorithm is performed, and the average time efficiency is 13 pfs, which can realize the real-time performance of the detection process.

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Online Road Detection under a Shadowy Traffic Image Using a Learning-Based Illumination-Independent Image

Cited by 7 publications

References 27 publications

Parametric Jensen-Shannon Statistical Complexity and Its Applications on Full-Scale Compartment Fire Data

Parametric Jensen-Shannon Statistical Complexity and Its Applications on Full-Scale Compartment Fire Data

Road Extraction Method of Remote Sensing Image Based on Deformable Attention Transformer

Automatic Detection and Classification of Road, Car, and Pedestrian Using Binocular Cameras in Traffic Scenes with a Common Framework

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