On the influence of artificially distorted images in firearm detection performance using deep learning
Detecting people carrying firearms in outdoor or indoor scenes usually identifies (or avoids) potentially dangerous situations. Nevertheless, the automatic detection of these weapons can be greatly affected by the scene conditions. Commonly, in real scenes these firearms can be seen from different p...
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PeerJ Inc.
2024-10-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2381.pdf |
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| author | Patricia Corral-Sanz Alvaro Barreiro-Garrido A. Belen Moreno Angel Sanchez |
| author_facet | Patricia Corral-Sanz Alvaro Barreiro-Garrido A. Belen Moreno Angel Sanchez |
| author_sort | Patricia Corral-Sanz |
| collection | DOAJ |
| description | Detecting people carrying firearms in outdoor or indoor scenes usually identifies (or avoids) potentially dangerous situations. Nevertheless, the automatic detection of these weapons can be greatly affected by the scene conditions. Commonly, in real scenes these firearms can be seen from different perspectives. They also may have different real and apparent sizes. Moreover, the images containing these targets are usually cluttered, and firearms can appear as partially occluded. It is also common that the images can be affected by several types of distortions such as impulse noise, image darkening or blurring. All these perceived variabilities could significantly degrade the accuracy of firearm detection. Current deep detection networks offer good classification accuracy, with high efficiency and under constrained computational resources. However, the influence of practical conditions in which the objects are to be detected has not sufficiently been analyzed. Our article describes an experimental study on how a set of selected image distortions quantitatively degrade the detection performance on test images when the detection networks have only been trained with images that do not present the alterations. The analyzed test image distortions include impulse noise, blurring (or defocus), image darkening, image shrinking and occlusions. In order to quantify the impact of each individual distortion on the firearm detection problem, we have used a standard YOLOv5 network. Our experimental results have shown that the increased addition of impulse salt-and-pepper noise is by far the distortion that affects the most the performance of the detection network. |
| format | Article |
| id | doaj-art-9e3e1e28e4b6471db37af7b29bde6327 |
| institution | OA Journals |
| issn | 2376-5992 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | PeerJ Inc. |
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| series | PeerJ Computer Science |
| spelling | doaj-art-9e3e1e28e4b6471db37af7b29bde63272025-08-20T02:17:57ZengPeerJ Inc.PeerJ Computer Science2376-59922024-10-0110e238110.7717/peerj-cs.2381On the influence of artificially distorted images in firearm detection performance using deep learningPatricia Corral-Sanz0Alvaro Barreiro-Garrido1A. Belen Moreno2Angel Sanchez3Department of Computer Science and Statistics, Universidad Rey Juan Carlos, Mostoles, Madrid, SpainDepartment of Computer Science and Statistics, Universidad Rey Juan Carlos, Mostoles, Madrid, SpainDepartment of Computer Science and Statistics, Universidad Rey Juan Carlos, Mostoles, Madrid, SpainDepartment of Computer Science and Statistics, Universidad Rey Juan Carlos, Mostoles, Madrid, SpainDetecting people carrying firearms in outdoor or indoor scenes usually identifies (or avoids) potentially dangerous situations. Nevertheless, the automatic detection of these weapons can be greatly affected by the scene conditions. Commonly, in real scenes these firearms can be seen from different perspectives. They also may have different real and apparent sizes. Moreover, the images containing these targets are usually cluttered, and firearms can appear as partially occluded. It is also common that the images can be affected by several types of distortions such as impulse noise, image darkening or blurring. All these perceived variabilities could significantly degrade the accuracy of firearm detection. Current deep detection networks offer good classification accuracy, with high efficiency and under constrained computational resources. However, the influence of practical conditions in which the objects are to be detected has not sufficiently been analyzed. Our article describes an experimental study on how a set of selected image distortions quantitatively degrade the detection performance on test images when the detection networks have only been trained with images that do not present the alterations. The analyzed test image distortions include impulse noise, blurring (or defocus), image darkening, image shrinking and occlusions. In order to quantify the impact of each individual distortion on the firearm detection problem, we have used a standard YOLOv5 network. Our experimental results have shown that the increased addition of impulse salt-and-pepper noise is by far the distortion that affects the most the performance of the detection network.https://peerj.com/articles/cs-2381.pdfArtificial image distortionsFirearm detectionDeep learningYOLOObject detection metrics |
| spellingShingle | Patricia Corral-Sanz Alvaro Barreiro-Garrido A. Belen Moreno Angel Sanchez On the influence of artificially distorted images in firearm detection performance using deep learning PeerJ Computer Science Artificial image distortions Firearm detection Deep learning YOLO Object detection metrics |
| title | On the influence of artificially distorted images in firearm detection performance using deep learning |
| title_full | On the influence of artificially distorted images in firearm detection performance using deep learning |
| title_fullStr | On the influence of artificially distorted images in firearm detection performance using deep learning |
| title_full_unstemmed | On the influence of artificially distorted images in firearm detection performance using deep learning |
| title_short | On the influence of artificially distorted images in firearm detection performance using deep learning |
| title_sort | on the influence of artificially distorted images in firearm detection performance using deep learning |
| topic | Artificial image distortions Firearm detection Deep learning YOLO Object detection metrics |
| url | https://peerj.com/articles/cs-2381.pdf |
| work_keys_str_mv | AT patriciacorralsanz ontheinfluenceofartificiallydistortedimagesinfirearmdetectionperformanceusingdeeplearning AT alvarobarreirogarrido ontheinfluenceofartificiallydistortedimagesinfirearmdetectionperformanceusingdeeplearning AT abelenmoreno ontheinfluenceofartificiallydistortedimagesinfirearmdetectionperformanceusingdeeplearning AT angelsanchez ontheinfluenceofartificiallydistortedimagesinfirearmdetectionperformanceusingdeeplearning |