Hazardous material sign detection and recognition

Parra, Albert; Zhao, Bin; Haddad, Andrew W.; Boutin, Mireille; Delp, Edward J.

doi:10.1109/icip.2013.6738544

Cited by 6 publications

(4 citation statements)

References 24 publications

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“…The use of image recognition technologies by machine learning requires significant computing power, which is determined by the clock rate and number of processor cores, the amount of RAM and the power of the video information processing system. Thus, it will be optimal to use a PC with maximum characteristics [9][10][11].…”

Section: Materials and Methods Of Researchmentioning

confidence: 99%

Remote Visual Information System for Identification of Dangerous Substances Using Unmanned Aircrafts

Bychenko

Udovenko

Nuianzin

et al. 2022

Advances in Science and Technology

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Development of a functional model of the process of creating a knowledge base on the recognition of objects and actions of the enemy on the basis of neural networks and fuzzy logic. The aim of the work is to develop a set of software and hardware designed for remote identification of hazardous substances by machine visual recognition of information signs of dangerous goods with the output of relevant information to the means of visual display (interface). Recommendations concerning providing UAVs with the necessary technical means to monitor the zone of emergencies are analyzed. The recommendations of the organization of radio communication between the UAV and the operator depending on the range of the UAV departure, terrain conditions etc are analyzed and given. The structural scheme of the complex of remote recognition of HC in the form of blocks, units and software and hardware is developed. As a result of the analysis of programming systems, it was found that Python programming language is the best choice to ensure the full operation of the software due to the built-in capabilities and the involvement of third-party frameworks. A database containing information on more than 3.000 HCs with detailed recommendations for emergency response is developed. The hardware and software complex for remote identification of dangerous substances by machine visual recognition of information signs of dangerous goods by UAV, consisting of unmanned aerial platform with photo-video recording means, data transmission system to ground control station, PC for processing results and related software are substantiated and developed. A test of the UAV's capabilities in recognizing danger signs with UN numbers in different lighting conditions was tested. In all cases, the HC was accurately identified. The ideas and methods proposed in this article will allow to create cheap and simple tools for rescue units of Ukraine, which deal with the consequences of emergencies related to the leakage of HCs.

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Section: Materials and Methods Of Researchmentioning

confidence: 99%

Remote Visual Information System for Identification of Dangerous Substances Using Unmanned Aircrafts

Bychenko

Udovenko

Nuianzin

et al. 2022

Advances in Science and Technology

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“…Those methods, however, can be affected by perspective distortion from different viewpoints. Saliency-based methods use saliency detection to speed up the detection process and get the region of interest where the hazmat sign might locate in [15] [16]. Recent research focued on CNN methods, such as [17] [18].…”

Section: A Hazmat Sign Detectionmentioning

confidence: 99%

Improving CNN-based Planar Object Detection with Geometric Prior Knowledge

Cai,

Hou,

et al. 2019

Preprint

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In this paper, we focus on the question: how might mobile robots take advantage of affordable RGB-D sensors for object detection? Although current CNN-based object detectors have achieved impressive results, there are three main drawbacks for practical usage on mobile robots: 1) It is hard and time-consuming to collect and annotate large-scale training sets.2) It usually needs a long training time. 3) CNN-based object detection shows significant weakness in predicting location. We propose a novel approach for the detection of planar objects, which rectifies images with geometric information to compensate for the perspective distortion before feeding it to the CNN detector module, typically a CNN-based detector like YOLO or MASK RCNN. By dealing with the perspective distortion in advance, we eliminate the need for the CNN detector to learn that. Experiments show that this approach significantly boosts the detection performance. Besides, it effectively reduces the number of training images required. In addition to the novel detection framework proposed, we also release an RGB-D dataset for hazmat sign detection. To the best of our knowledge, this is the first public-available hazmat sign detection dataset with RGB-D sensors.

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“…Ellena et al [5] quantified the characteristics of hazard markers and compared them with a candidate picture for hazmat sign detection. Based on the geometric onstraints and saliency maps, Parra et al [6] introduced two hazmat sign recognition methods. Based on character-specific extremal regions, Gou et al [7] proposed a vehicle license plate recognition method by hybrid discriminative restricted Boltzmann machines.…”

Section: Introductionmentioning

confidence: 99%

HMD-Net: A Vehicle Hazmat Marker Detection Benchmark

Jia

Wang

Wang³

et al. 2022

Entropy

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Vehicles carrying hazardous material (hazmat) are severe threats to the safety of highway transportation, and a model that can automatically recognize hazmat markers installed or attached on vehicles is essential for intelligent management systems. However, there is still no public dataset for benchmarking the task of hazmat marker detection. To this end, this paper releases a large-scale vehicle hazmat marker dataset named VisInt-VHM, which includes 10,000 images with a total of 20,023 hazmat markers captured under different environmental conditions from a real-world highway. Meanwhile, we provide an compact hazmat marker detection network named HMD-Net, which utilizes a revised lightweight backbone and is further compressed by channel pruning. As a consequence, the trained-model can be efficiently deployed on a resource-restricted edge device. Experimental results demonstrate that compared with some established methods such as YOLOv3, YOLOv4, their lightweight versions and popular lightweight models, HMD-Net can achieve a better trade-off between the detection accuracy and the inference speed.

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Hazardous material sign detection and recognition

Cited by 6 publications

References 24 publications

Remote Visual Information System for Identification of Dangerous Substances Using Unmanned Aircrafts

Remote Visual Information System for Identification of Dangerous Substances Using Unmanned Aircrafts

Improving CNN-based Planar Object Detection with Geometric Prior Knowledge

HMD-Net: A Vehicle Hazmat Marker Detection Benchmark

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