Matches in SemOpenAlex for { <https://semopenalex.org/work/W4385378081> ?p ?o ?g. }
Showing items 1 to 57 of
57
with 100 items per page.
- W4385378081 abstract "<strong class=journal-contentHeaderColor>Abstract.</strong> With the advancement of computer vision, artificial intelligence, and remote sensing technologies, deep learning algorithms are increasingly used in terrestrial, airborne, and spaceborne-based fire detection systems. The performance and generalization of these data-driven fire detection algorithms, however, are restricted by the limited number and source of fire detection datasets. A large-scale fire detection benchmark dataset covering complex and varied fire scenarios is urgently needed. This work constructs a 100,000-level Flame and Smoke Detection Dataset (FASDD) based on multi-source heterogeneous flame and smoke images. It holds rich variations in image size, resolution, illumination (day and night), scenario (indoor and outdoor), image range (far and near), viewing angle (top view and side view), platform (surveillance cameras, drones, and satellites), and data source (Internet, social media, and open-access fire datasets). To the best of our knowledge, FASDD is currently the most versatile and comprehensive dataset for fire detection. It provides a challenging benchmark to drive the continuous evolution of fire detection models. Additionally, we formulate a unified workflow for preprocessing, annotation, and quality control of fire samples. Out-of-the-box annotations are published in four different formats for training deep learning models. Extensive performance evaluations based on classical methods show that most of the object detection models trained on FASDD can achieve satisfactory fire detection results, and especially YOLOv5x achieves nearly 80 % mAP@0.5 accuracy on heterogeneous images spanning two domains of computer vision and remote sensing. And the application in wildfire location demonstrates that deep learning models trained on our dataset can be used in recognizing and monitoring forest fires. Deep learning models trained with FASDD can be simultaneously deployed on satellites, drones, and ground sensors, thus realizing collaborative fire observation and detection in a space-air-ground integrated network environment. The dataset is available from the Science Data Bank website at <a href=https://doi.org/10.57760/sciencedb.j00104.00103 target=_blank rel=noopener>https://doi.org/10.57760/sciencedb.j00104.00103</a> (Wang et al., 2022a)." @default.
- W4385378081 created "2023-07-30" @default.
- W4385378081 date "2023-07-30" @default.
- W4385378081 modified "2023-10-01" @default.
- W4385378081 title "Comment on essd-2023-73" @default.
- W4385378081 doi "https://doi.org/10.5194/essd-2023-73-cc11" @default.
- W4385378081 hasPublicationYear "2023" @default.
- W4385378081 type Work @default.
- W4385378081 citedByCount "0" @default.
- W4385378081 crossrefType "peer-review" @default.
- W4385378081 hasBestOaLocation W43853780811 @default.
- W4385378081 hasConcept C108583219 @default.
- W4385378081 hasConcept C121332964 @default.
- W4385378081 hasConcept C153180895 @default.
- W4385378081 hasConcept C154945302 @default.
- W4385378081 hasConcept C177212765 @default.
- W4385378081 hasConcept C185798385 @default.
- W4385378081 hasConcept C205649164 @default.
- W4385378081 hasConcept C2776151529 @default.
- W4385378081 hasConcept C2780836893 @default.
- W4385378081 hasConcept C34736171 @default.
- W4385378081 hasConcept C41008148 @default.
- W4385378081 hasConcept C58640448 @default.
- W4385378081 hasConcept C62649853 @default.
- W4385378081 hasConcept C77088390 @default.
- W4385378081 hasConcept C97355855 @default.
- W4385378081 hasConceptScore W4385378081C108583219 @default.
- W4385378081 hasConceptScore W4385378081C121332964 @default.
- W4385378081 hasConceptScore W4385378081C153180895 @default.
- W4385378081 hasConceptScore W4385378081C154945302 @default.
- W4385378081 hasConceptScore W4385378081C177212765 @default.
- W4385378081 hasConceptScore W4385378081C185798385 @default.
- W4385378081 hasConceptScore W4385378081C205649164 @default.
- W4385378081 hasConceptScore W4385378081C2776151529 @default.
- W4385378081 hasConceptScore W4385378081C2780836893 @default.
- W4385378081 hasConceptScore W4385378081C34736171 @default.
- W4385378081 hasConceptScore W4385378081C41008148 @default.
- W4385378081 hasConceptScore W4385378081C58640448 @default.
- W4385378081 hasConceptScore W4385378081C62649853 @default.
- W4385378081 hasConceptScore W4385378081C77088390 @default.
- W4385378081 hasConceptScore W4385378081C97355855 @default.
- W4385378081 hasLocation W43853780811 @default.
- W4385378081 hasOpenAccess W4385378081 @default.
- W4385378081 hasPrimaryLocation W43853780811 @default.
- W4385378081 hasRelatedWork W2883677709 @default.
- W4385378081 hasRelatedWork W2908939556 @default.
- W4385378081 hasRelatedWork W2970686063 @default.
- W4385378081 hasRelatedWork W3201484345 @default.
- W4385378081 hasRelatedWork W4281945544 @default.
- W4385378081 hasRelatedWork W4283529202 @default.
- W4385378081 hasRelatedWork W4285335027 @default.
- W4385378081 hasRelatedWork W4288040045 @default.
- W4385378081 hasRelatedWork W4313289316 @default.
- W4385378081 hasRelatedWork W4377967120 @default.
- W4385378081 isParatext "false" @default.
- W4385378081 isRetracted "false" @default.
- W4385378081 workType "peer-review" @default.