Smoke vehicle detection based on multi-feature fusion and hidden Markov model

Tao, Huanjie; Lu, Xiaobo

doi:10.1007/s11554-019-00856-z

Cited by 25 publications

(11 citation statements)

References 37 publications

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“…Huo et al [6] proposed a multi-scale object detection algorithm for image-based smoke detection based on deep separable convolutional neural networks by exploiting feature pyramids and depth-wise separable convolutions. In terms of vehicle exhaust detection, Tao and Lu [7] proposed a smoky vehicle detection method based on multi-feature fusion and Hidden Markov Model (HMM). Cao et al used an image classification network to classify whether there is exhaust in motor vehicle pictures [3] , and the proposed motor vehicle exhaust detection method is an image classification method, and its detection of motor vehicle exhaust also depends on the results of vehicle positioning and segmentation , is not an end-to-end model.…”

Section: Relation Workmentioning

confidence: 99%

Smoky vehicle detection based on YOLOv5 and car suppression

Yu,

XiangQuan,

Chen

2023

Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023)

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Smoky vehicle detection plays an important role in controlling urban pollution and protecting the ecological environment. Due to the complex shape transformation of smoke and the influence of environment, lighting and other factors, recognizing smoke from images is still a major challenge, and there have been many corresponding studies in recent years. Based on the above problems, we collected 2361 pictures of smoky cars as a smoke detection data set using the on-board camera. In addition, we used Yolov5 as the basic model and proposed an improved Yolov5 algorithm by preprocessing and expanding the data. The experimental results show that compared with the initial model, the smoke detection map of the improved algorithm is improved by 1.7%.

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Section: Relation Workmentioning

confidence: 99%

Smoky vehicle detection based on YOLOv5 and car suppression

Yu,

XiangQuan,

Chen

2023

Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023)

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“…In this section, the residual data are considered to be a part of the observed data to fit the HMM. 28 The state of PDP is divided into good state (state 0), warning state (state 1), and fault state (state 2), and only the fault state is observable. This article models the state process ( X t : t ∈ R + ) as a continuous-time homogeneous Markov chain with a spatial state {0, 1, 2}.…”

Section: Hidden Markov Modeling and Estimation Of Parametersmentioning

confidence: 99%

Model parameter estimation and residual life span prediction of pneumatic diaphragm pump based on hidden Markov model in intelligent spraying

Yan

Chen

et al. 2019

International Journal of Advanced Robotic Systems

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Pneumatic diaphragm pump is an important part in intelligent spraying. When pneumatic diaphragm pump does not work normally, the entire intelligent spraying product line will be malfunctioned. To maintain and manage pneumatic diaphragm pump effectively, the grade analysis of the health status of pneumatic diaphragm pump is generally used according to its working state. Due to the effects of condition monitoring and random faults, some observable health predictions are often inaccurate. There are very few papers dealing with the health monitoring of pneumatic diaphragm pump and their estimation of residual life span. In this article, a method with vector autoregressive model and continuous-time hidden Markov model was proposed to analyze and evaluate the life span of pneumatic diaphragm pump based on the estimation error of the health condition and the cumulative deterioration of pneumatic diaphragm pump. It is modeled through a continuous-time Markov chain with three states, which includes unobservable healthy state 0, unobservable warning state 1, and observable fault state 2. The expectation–maximization algorithm is used to estimate the model parameters of the fitted hidden Markov. Through the posterior probability of pneumatic diaphragm pump in warning state 1, the derived conditional reliability function and mean residual life span formula can be calculated to evaluate the residual life span of pneumatic diaphragm pump. The results showed that the method can effectively predict the residual life span of pneumatic diaphragm pump, illustrate the effectiveness of the model, and improve the accuracy of the health status rating.

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“…Recent advanced methods including remote sensing vehicle exhaust monitoring with ultraviolet-infrared light, and automatic detection in surveillance videos [3]. Due to the cost of remote sensing with ultraviolet-infrared light is extremely expensive, detecting smoky vehicles in surveillance videos has attracted increasing attention recently [4,5,6,7,8,9,10,11] which essentially is to detect the visible black smoke and vehicles.…”

Section: Introductionmentioning

confidence: 99%

“…Though large progresses have been made in recent years [4,5,6,7,8,9,10,11], the false alarm rate over frames is still high (e.g., around 13% in [3]) which is hard to meet practical applications. Worse still, unlike the wildfire smoke recognition, there is no public available vehicle smoke data in the community.…”

Section: Introductionmentioning

confidence: 99%

Video-based Smoky Vehicle Detection with A Coarse-to-Fine Framework

Peng¹,

Fan²,

Wu³

et al. 2022

Preprint

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Automatic smoky vehicle detection in videos is a superior solution to the traditional expensive remote sensing one with ultraviolet-infrared light devices for environmental protection agencies. However, it is challenging to distinguish vehicle smoke from shadow and wet regions coming from rear vehicle or clutter roads, and could be worse due to limited annotated data. In this paper, we first introduce a real-world large-scale smoky vehicle dataset with 75,000 annotated smoky vehicle images, facilitating the effective training of advanced deep learning models. To enable fair algorithm comparison, we also build a smoky vehicle video dataset including 163 long videos with segment-level annotations. Moreover, we present a new Coarse-to-fine Deep Smoky vehicle detection (CoDeS) framework for efficient smoky vehicle detection. The CoDeS first leverages a light-weight YOLO detector for fast smoke detection with high recall rate, and then applies a smoke-vehicle matching strategy to eliminate non-vehicle smoke, and finally uses a elaborately-designed 3D model to further refine the results in spatial temporal space. Extensive experiments in four metrics demonstrate that our framework is significantly superior to those hand-crafted feature based methods and recent advanced methods. The code and dataset will be released at https://github.com/pengxj/smokyvehicle.

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Smoke vehicle detection based on multi-feature fusion and hidden Markov model

Cited by 25 publications

References 37 publications

Smoky vehicle detection based on YOLOv5 and car suppression

Smoky vehicle detection based on YOLOv5 and car suppression

Model parameter estimation and residual life span prediction of pneumatic diaphragm pump based on hidden Markov model in intelligent spraying

Video-based Smoky Vehicle Detection with A Coarse-to-Fine Framework

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