Computer vision-based approach for smart traffic condition assessment at the railroad grade crossing

Guo, Feng; Wang, Yi; Qian, Yu

doi:10.1016/j.aei.2021.101456

Cited by 23 publications

(9 citation statements)

References 21 publications

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“…The SPP layer is positioned on top of the final convolution layer to prevent the engagement of cropping and warping, which creates a new network named as SPP-net [48]. Spatial pyramid pooling (SPP) is considered to be a cutting-edge methodology for assessing pooling layer efficiency [49]. The SPP layer is designed to match the size of the feature maps in the SPP network, and the number of spatial bins remains constant at each pyramid level.…”

Section: Spatial Pyramid Pooling Methodsmentioning

confidence: 99%

A Comparison of Pooling Methods for Convolutional Neural Networks

et al. 2022

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One of the most promising techniques used in various sciences is deep neural networks (DNNs). A special type of DNN called a convolutional neural network (CNN) consists of several convolutional layers, each preceded by an activation function and a pooling layer. The feature map of the previous layer is sampled by the pooling layer (that seems to be an important layer) to create a new feature map with condensed resolution. This layer significantly reduces the spatial dimension of the input. It always accomplished two main goals. As a first step, it reduces the number of parameters or weights to minimize computational costs. The second step is to prevent the overfitting of the network. In addition, pooling techniques can significantly reduce model training time and computational costs. This paper provides a critical understanding of traditional and modern pooling techniques and highlights the strengths and weaknesses for readers. Moreover, the performance of pooling techniques on different datasets is qualitatively evaluated and reviewed. This study is expected to contribute to a comprehensive understanding of the importance of CNNs and pooling techniques in computer vision challenges.

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Section: Spatial Pyramid Pooling Methodsmentioning

confidence: 99%

A Comparison of Pooling Methods for Convolutional Neural Networks

et al. 2022

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“…Columbia, SC, revealed that all surveyed first responders had faced delays at crossings, sometimes up to 40 min. Early attempts to predict crossing clearance times used computer vision to estimate vehicle queues (Jiang et al, 2021;Guo et al, 2022a), while Guo et al (2022b) refined this approach with the DTDNet, a CNN designed to count vehicles accurately under various conditions.…”

Section: Our Recent Effort In Railroad Crossing Safety and Connected ...mentioning

confidence: 99%

Intelligent railroad inspection and monitoring

Qian

2024

Front. Built Environ.

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Railways are essential to the global transportation infrastructure, providing eco-friendly and economical solutions for the movement of freight and passengers. Inspecting and maintaining extensive rail networks timely poses significant challenges. My group and collaborators have focused on automated railroad inspection technologies, emphasizing the use of deep learning and computer vision to overcome the limitations of traditional manual inspections. Our research introduces groundbreaking real-time inspection methods, leveraging a specialized dataset of railroad components for enhanced instance segmentation models, achieving unprecedented accuracy and inference speeds. The developed computer vision systems efficiently detect track components and their changes over time, and also quantify rail surface defects. Additionally, our work extends to improving railroad crossing safety, utilizing deep learning frameworks for the detection of unusual pedestrian behaviors and object identification, aimed at reducing crossing incidents and improving emergency response times. Our future research directions aim to further refine the cost-effectiveness and autonomy of railroad inspection systems. Through these innovations, we hope to aid in the inspection and maintenance of railroads, offering practical solutions for railroad and other civil engineering applications.

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“…In recent years, CNN has gained more attention to solve automatic for automatic detection and evaluation in civil engineering applications, such as bridge crack segmentation [12], defect detection on the metro tunnel surfaces [13], and smart traffic condition assessment [14]. Moreover, CNN also shows great potential and prospects in intelligent maintenance of infrastructure along railroad lines.…”

Section: Introductionmentioning

confidence: 99%

UAV imagery-based railroad station building inspection using hybrid learning architecture

Liu,

Yang,

et al. 2024

Meas. Sci. Technol.

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The apparent defects of railroad station building such as the broken glass, missing screw, and rust corrosion need to be inspected regularly and timely to ensure the safety of passenger. Unmanned Aerial Vehicle (UAV) imagery-based inspections have the potential to revolutionize current manual visual inspections by providing a better overhead view and mitigating safety concerns. This paper proposes a hybrid learning architecture called YOLOS (You Only Look Once Station Scene) to simultaneously detect and segment station building surface defects of UAV images. First, a novel Squeeze-and-Excitation (SE) attention block is integrated into the detection branch to adaptively learn the weights of the feature channels, thereby promoting the network to pay attention to the critical deep features of the objects. And then, a new semantic segmentation branch parallel to the detection branch is designed and assembled in YOLOS for pixel-level defect recognition. Finally, extensive experiments on railroad station dataset established with drone imagery prove the effectiveness and robustness of the SE-based network on station surface defect detection. This method can quickly convert UAV imagery into useful information with a high detection rate.

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Computer vision-based approach for smart traffic condition assessment at the railroad grade crossing

Cited by 23 publications

References 21 publications

A Comparison of Pooling Methods for Convolutional Neural Networks

A Comparison of Pooling Methods for Convolutional Neural Networks

Intelligent railroad inspection and monitoring

UAV imagery-based railroad station building inspection using hybrid learning architecture

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