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Grant support

This research has been possible thanks to the financing of RoboCity2030-DIH-CM, Madrid Robotics Digital Innovation Hub, S2018/NMT-4331, funded by "Programas de Actividades I+D en la Comunidad Madrid" and cofunded by Structural Funds of the EU and TASAR (Team of Advanced Search And Rescue Robots), funded by "Proyectos de I+D+i del Ministerio de Ciencia, Innovacion y Universidades" (PID2019-105808RB-I00). This research was developed in Centro de Automatica y Robotica-Universidad Politecnica de Madrid-Consejo Superior de Investigaciones Cientificas (CAR UPM-CSIC).

Analysis of institutional authors

Cruz Ulloa, ChristyanCorresponding AuthorBarrientos, AAuthorDel Cerro, JAuthor

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November 29, 2021
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Autonomous Thermal Vision Robotic System for Victims Recognition in Search and Rescue Missions

Publicated to:Sensors. 21 (21): 7346-7346 - 2021-11-01 21(21), DOI: 10.3390/s21217346

Authors: Ulloa, CC; Sánchez, GP; Barrientos, A; Del Cerro, J

Affiliations

Univ Politecn Madrid, Ctr Automat & Robot CAR, CSIC, Madrid 28006, Spain - Author

Abstract

Technological breakthroughs in recent years have led to a revolution in fields such as Machine Vision and Search and Rescue Robotics (SAR), thanks to the application and development of new and improved neural networks to vision models together with modern optical sensors that incorporate thermal cameras, capable of capturing data in post-disaster environments (PDE) with rustic conditions (low luminosity, suspended particles, obstructive materials). Due to the high risk posed by PDE because of the potential collapse of structures, electrical hazards, gas leakage, etc., primary intervention tasks such as victim identification are carried out by robotic teams, provided with specific sensors such as thermal, RGB cameras, and laser. The application of Convolutional Neural Networks (CNN) to computer vision is a breakthrough for detection algorithms. Conventional methods for victim identification in these environments use RGB image processing or trained dogs, but detection with RGB images is inefficient in the absence of light or presence of debris; on the other hand, developments with thermal images are limited to the field of surveillance. This paper's main contribution focuses on implementing a novel automatic method based on thermal image processing and CNN for victim identification in PDE, using a Robotic System that uses a quadruped robot for data capture and transmission to the central station. The robot's automatic data processing and control have been carried out through Robot Operating System (ROS). Several tests have been carried out in different environments to validate the proposed method, recreating PDE with varying conditions of light, from which the datasets have been generated for the training of three neural network models (Fast R-CNN, SSD, and YOLO). The method's efficiency has been tested against another method based on CNN and RGB images for the same task showing greater effectiveness in PDE main results show that the proposed method has an efficiency greater than 90%.

Keywords

AlgorithmsAnimalsComputer visionConditionConvolutionConvolutional neural networkConvolutional neural networksData handlingDogsEfficiencyEmissivityImage processing, computer-assistedNeural networks, computerPost disastersRescue workRgb imagesRobot operating systemRobotic systemsRoboticsRobotsRosSearch and rescue robotSearch and rescue robotsSearch enginesThermal imagesUnitree a1Victim identifications

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Sensors due to its progression and the good impact it has achieved in recent years, according to the agency Scopus (SJR), it has become a reference in its field. In the year of publication of the work, 2021, it was in position , thus managing to position itself as a Q1 (Primer Cuartil), in the category Analytical Chemistry.

From a relative perspective, and based on the normalized impact indicator calculated from World Citations provided by WoS (ESI, Clarivate), it yields a value for the citation normalization relative to the expected citation rate of: 1.26. This indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: ESI Nov 14, 2024)

This information is reinforced by other indicators of the same type, which, although dynamic over time and dependent on the set of average global citations at the time of their calculation, consistently position the work at some point among the top 50% most cited in its field:

  • Weighted Average of Normalized Impact by the Scopus agency: 1.72 (source consulted: FECYT Feb 2024)
  • Field Citation Ratio (FCR) from Dimensions: 11.5 (source consulted: Dimensions Jul 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-07-23, the following number of citations:

  • WoS: 19
  • Scopus: 31
  • Open Alex: 38

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-07-23:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 57.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 67 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 0.75.
  • The number of mentions on the social network X (formerly Twitter): 2 (Altmetric).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.

Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (Ulloa, CC) and Last Author (CERRO GINER, JAIME DEL).

the authors responsible for correspondence tasks have been CRUZ ULLOA, CHRISTYAN and Ulloa, CC.