Smoke Vehicle Detection Based on Spatiotemporal Bag-Of-Features and Professional Convolutional Neural Network

Tao, Huanjie; Lu, Xiaobo

doi:10.1109/tcsvt.2019.2920657

Cited by 17 publications

(7 citation statements)

References 67 publications

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“…Filonenko et al For spatial-temporal networks, Lin et al [27] designed a 3D CNN to extract the spatial-temporal features of smoke proposals. Tao et al [9] built a professional spatial-temporal model which is comprised of three CNNs on different orthogonal planes. Li et al [26] proposed a 3D parallel fully convolutional networks for real-time video based smoke detection.…”

Section: Visual Smoke Detectionmentioning

confidence: 99%

“…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%

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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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Section: Visual Smoke Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Peng¹,

Fan²,

Wu³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…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 being extremely expensive, detecting smoky vehicles in surveillance videos has recently attracted increasing attention [4][5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Cascaded Vehicle Matching and Short-Term Spatial-Temporal Network for Smoky Vehicle Detection

Peng

Fan

et al. 2023

Applied Sciences

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Vehicle exhaust is the main source of air pollution with the rapid increase of fuel vehicles. Automatic smoky vehicle detection in videos is a superior solution to traditional expensive remote sensing with ultraviolet-infrared light devices for environmental protection agencies. However, it is challenging to distinguish vehicle smoke from shadow and wet regions in cluttered 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 a fair algorithm comparison, we also built a smoky vehicle video dataset including 163 long videos with segment-level annotations. Second, we present a novel efficient cascaded framework for smoky vehicle detection which largely integrates prior knowledge and advanced deep networks. Specifically, it starts from an improved frame-based smoke detector with a high recall rate, and then applies a vehicle matching strategy to fast eliminate non-vehicle smoke proposals, and finally refines the detection with an elaborately-designed short-term spatial-temporal network in consecutive frames. Extensive experiments in four metrics demonstrated that our framework is significantly superior to hand-crafted feature based methods and recent advanced methods.

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“…In the third set of experiments, we investigated the selection of post-processing schemes to combine with an 11th algorithm, which was a machine learning approach for vehicle detection. Inspired by some similar approaches such as spatial analysis [36] and smoke vehicle detection [37,38], besides the ten detection algorithms [25,[27][28][29][30][31][32][33][34][35], we adapted a two-stage machine learning approach from the tiramisu code [39] for semantic segmentation to perform the classification task via a hundred-layer densely connected convolutional network (DenseNets). The three post-processing schemes [4,14,20] that performed best in the first set of experiments were each applied to the machine learning approach.…”

Section: Introductionmentioning

confidence: 99%

Selecting Post-Processing Schemes for Accurate Detection of Small Objects in Low-Resolution Wide-Area Aerial Imagery

Gao

Ram

Philip³

et al. 2022

Remote Sensing

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In low-resolution wide-area aerial imagery, object detection algorithms are categorized as feature extraction and machine learning approaches, where the former often requires a post-processing scheme to reduce false detections and the latter demands multi-stage learning followed by post-processing. In this paper, we present an approach on how to select post-processing schemes for aerial object detection. We evaluated combinations of each of ten vehicle detection algorithms with any of seven post-processing schemes, where the best three schemes for each algorithm were determined using average F-score metric. The performance improvement is quantified using basic information retrieval metrics as well as the classification of events, activities and relationships (CLEAR) metrics. We also implemented a two-stage learning algorithm using a hundred-layer densely connected convolutional neural network for small object detection and evaluated its degree of improvement when combined with the various post-processing schemes. The highest average F-scores after post-processing are 0.902, 0.704 and 0.891 for the Tucson, Phoenix and online VEDAI datasets, respectively. The combined results prove that our enhanced three-stage post-processing scheme achieves a mean average precision (mAP) of 63.9% for feature extraction methods and 82.8% for the machine learning approach.

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Smoke Vehicle Detection Based on Spatiotemporal Bag-Of-Features and Professional Convolutional Neural Network

Cited by 17 publications

References 67 publications

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

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

Cascaded Vehicle Matching and Short-Term Spatial-Temporal Network for Smoky Vehicle Detection

Selecting Post-Processing Schemes for Accurate Detection of Small Objects in Low-Resolution Wide-Area Aerial Imagery

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