FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4386814307> ?p ?o ?g. }

• W4386814307 endingPage "1887" @default.
• W4386814307 startingPage "1887" @default.
• W4386814307 abstract "Forest fires pose severe risks, including habitat loss and air pollution. Accurate forest flame segmentation is vital for effective fire management and protection of ecosystems. It improves detection, response, and understanding of fire behavior. Due to the easy accessibility and rich information content of forest remote sensing images, remote sensing techniques are frequently applied in forest flame segmentation. With the advancement of deep learning, convolutional neural network (CNN) techniques have been widely adopted for forest flame segmentation and have achieved remarkable results. However, forest remote sensing images often have high resolutions, and relative to the entire image, forest flame regions are relatively small, resulting in class imbalance issues. Additionally, mainstream semantic segmentation methods are limited by the receptive field of CNNs, making it challenging to effectively extract global features from the images and leading to poor segmentation performance when relying solely on labeled datasets. To address these issues, we propose a method based on the deeplabV3+ model, incorporating the following design strategies: (1) an adaptive Copy-Paste data augmentation method is introduced to learn from challenging samples (Images that cannot be adequately learned due to class imbalance and other factors) effectively, (2) transformer modules are concatenated and parallelly integrated into the encoder, while a CBAM attention mechanism is added to the decoder to fully extract image features, and (3) a dice loss is introduced to mitigate the class imbalance problem. By conducting validation on our self-constructed dataset, our approach has demonstrated superior performance across multiple metrics compared to current state-of-the-art semantic segmentation methods. Specifically, in terms of IoU (Intersection over Union), Precision, and Recall metrics for the flame category, our method has exhibited notable enhancements of 4.09%, 3.48%, and 1.49%, respectively, when compared to the best-performing UNet model. Moreover, our approach has achieved advancements of 11.03%, 9.10%, and 4.77% in the same aforementioned metrics as compared to the baseline model." @default.
• W4386814307 created "2023-09-18" @default.
• W4386814307 creator A5032663320 @default.
• W4386814307 creator A5058852174 @default.
• W4386814307 creator A5059022566 @default.
• W4386814307 creator A5063059166 @default.
• W4386814307 date "2023-09-17" @default.
• W4386814307 modified "2023-09-27" @default.
• W4386814307 title "FlameTransNet: Advancing Forest Flame Segmentation with Fusion and Augmentation Techniques" @default.
• W4386814307 cites W1903029394 @default.
• W4386814307 cites W2499316477 @default.
• W4386814307 cites W2560023338 @default.
• W4386814307 cites W2570372101 @default.
• W4386814307 cites W2604292667 @default.
• W4386814307 cites W2752782242 @default.
• W4386814307 cites W2766865821 @default.
• W4386814307 cites W2805890692 @default.
• W4386814307 cites W2866634454 @default.
• W4386814307 cites W2884585870 @default.
• W4386814307 cites W2922306415 @default.
• W4386814307 cites W2928976932 @default.
• W4386814307 cites W2955202322 @default.
• W4386814307 cites W2958837267 @default.
• W4386814307 cites W2963163009 @default.
• W4386814307 cites W2964309882 @default.
• W4386814307 cites W2973227561 @default.
• W4386814307 cites W2978858971 @default.
• W4386814307 cites W3011462284 @default.
• W4386814307 cites W3100733145 @default.
• W4386814307 cites W3106881551 @default.
• W4386814307 cites W3116516111 @default.
• W4386814307 cites W3122830743 @default.
• W4386814307 cites W3124022875 @default.
• W4386814307 cites W3124539583 @default.
• W4386814307 cites W3127859904 @default.
• W4386814307 cites W3136217325 @default.
• W4386814307 cites W3168997536 @default.
• W4386814307 cites W3176659256 @default.
• W4386814307 cites W3198090248 @default.
• W4386814307 cites W4206043725 @default.
• W4386814307 cites W4283808550 @default.
• W4386814307 cites W4286213100 @default.
• W4386814307 cites W4315572981 @default.
• W4386814307 cites W4322631170 @default.
• W4386814307 cites W4367599058 @default.
• W4386814307 cites W4380193920 @default.
• W4386814307 cites W4381299201 @default.
• W4386814307 cites W4382400884 @default.
• W4386814307 cites W4383721837 @default.
• W4386814307 doi "https://doi.org/10.3390/f14091887" @default.
• W4386814307 hasPublicationYear "2023" @default.
• W4386814307 type Work @default.
• W4386814307 citedByCount "0" @default.
• W4386814307 crossrefType "journal-article" @default.
• W4386814307 hasAuthorship W4386814307A5032663320 @default.
• W4386814307 hasAuthorship W4386814307A5058852174 @default.
• W4386814307 hasAuthorship W4386814307A5059022566 @default.
• W4386814307 hasAuthorship W4386814307A5063059166 @default.
• W4386814307 hasBestOaLocation W43868143071 @default.
• W4386814307 hasConcept C108583219 @default.
• W4386814307 hasConcept C111919701 @default.
• W4386814307 hasConcept C118505674 @default.
• W4386814307 hasConcept C124504099 @default.
• W4386814307 hasConcept C153180895 @default.
• W4386814307 hasConcept C154945302 @default.
• W4386814307 hasConcept C205649164 @default.
• W4386814307 hasConcept C41008148 @default.
• W4386814307 hasConcept C62649853 @default.
• W4386814307 hasConcept C81363708 @default.
• W4386814307 hasConcept C89600930 @default.
• W4386814307 hasConceptScore W4386814307C108583219 @default.
• W4386814307 hasConceptScore W4386814307C111919701 @default.
• W4386814307 hasConceptScore W4386814307C118505674 @default.
• W4386814307 hasConceptScore W4386814307C124504099 @default.
• W4386814307 hasConceptScore W4386814307C153180895 @default.
• W4386814307 hasConceptScore W4386814307C154945302 @default.
• W4386814307 hasConceptScore W4386814307C205649164 @default.
• W4386814307 hasConceptScore W4386814307C41008148 @default.
• W4386814307 hasConceptScore W4386814307C62649853 @default.
• W4386814307 hasConceptScore W4386814307C81363708 @default.
• W4386814307 hasConceptScore W4386814307C89600930 @default.
• W4386814307 hasIssue "9" @default.
• W4386814307 hasLocation W43868143071 @default.
• W4386814307 hasOpenAccess W4386814307 @default.
• W4386814307 hasPrimaryLocation W43868143071 @default.
• W4386814307 hasRelatedWork W2731899572 @default.
• W4386814307 hasRelatedWork W2790662084 @default.
• W4386814307 hasRelatedWork W2999805992 @default.
• W4386814307 hasRelatedWork W3116150086 @default.
• W4386814307 hasRelatedWork W3133861977 @default.
• W4386814307 hasRelatedWork W4200173597 @default.
• W4386814307 hasRelatedWork W4285827401 @default.
• W4386814307 hasRelatedWork W4291897433 @default.
• W4386814307 hasRelatedWork W4312417841 @default.
• W4386814307 hasRelatedWork W4321369474 @default.
• W4386814307 hasVolume "14" @default.
• W4386814307 isParatext "false" @default.
• W4386814307 isRetracted "false" @default.

• next