Matches in SemOpenAlex for { <https://semopenalex.org/work/W4362679331> ?p ?o ?g. }
Showing items 1 to 71 of
71
with 100 items per page.
- W4362679331 abstract "Pest counting, which predicts the number of pests in the early stage, is very important because it enables rapid pest control, reduces damage to crops, and improves productivity. In recent years, light traps have been increasingly used to lure and photograph pests for pest counting. However, pest images have a wide range of variability in pest appearance owing to severe occlusion, wide pose variation, and even scale variation. This makes pest counting more challenging. To address these issues, this study proposes a new pest counting model referred to as multiscale and deformable attention CenterNet (Mada-CenterNet) for internal low-resolution (LR) and high-resolution (HR) joint feature learning. Compared with the conventional CenterNet, the proposed Mada-CenterNet adopts a multiscale heatmap generation approach in a two-step fashion to predict LR and HR heatmaps adaptively learned to scale variations, that is, changes in the number of pests. In addition, to overcome the pose and occlusion problems, a new between-hourglass skip connection based on deformable and multiscale attention is designed to ensure internal LR and HR joint feature learning and incorporate geometric deformation, thereby resulting in an improved pest counting accuracy. Through experiments, the proposed Mada-CenterNet is verified to generate the HR heatmap more accurately and improve pest counting accuracy owing to multiscale heatmap generation, joint internal feature learning, and deformable and multiscale attention. In addition, the proposed model is confirmed to be effective in overcoming severe occlusions and variations in pose and scale. The experimental results show that the proposed model outperforms state-of-the-art crowd counting and object detection models." @default.
- W4362679331 created "2023-04-07" @default.
- W4362679331 creator A5046010934 @default.
- W4362679331 creator A5085235647 @default.
- W4362679331 date "2023-04-05" @default.
- W4362679331 modified "2023-10-16" @default.
- W4362679331 title "Trap-Based Pest Counting: Multiscale and Deformable Attention CenterNet Integrating Internal LR and HR Joint Feature Learning" @default.
- W4362679331 doi "https://doi.org/10.48550/arxiv.2304.02291" @default.
- W4362679331 hasPublicationYear "2023" @default.
- W4362679331 type Work @default.
- W4362679331 citedByCount "0" @default.
- W4362679331 crossrefType "posted-content" @default.
- W4362679331 hasAuthorship W4362679331A5046010934 @default.
- W4362679331 hasAuthorship W4362679331A5085235647 @default.
- W4362679331 hasBestOaLocation W43626793311 @default.
- W4362679331 hasConcept C119857082 @default.
- W4362679331 hasConcept C121332964 @default.
- W4362679331 hasConcept C127413603 @default.
- W4362679331 hasConcept C138885662 @default.
- W4362679331 hasConcept C153180895 @default.
- W4362679331 hasConcept C154945302 @default.
- W4362679331 hasConcept C159985019 @default.
- W4362679331 hasConcept C170154142 @default.
- W4362679331 hasConcept C18555067 @default.
- W4362679331 hasConcept C192562407 @default.
- W4362679331 hasConcept C204323151 @default.
- W4362679331 hasConcept C22508944 @default.
- W4362679331 hasConcept C2776401178 @default.
- W4362679331 hasConcept C2778755073 @default.
- W4362679331 hasConcept C31972630 @default.
- W4362679331 hasConcept C41008148 @default.
- W4362679331 hasConcept C41895202 @default.
- W4362679331 hasConcept C59822182 @default.
- W4362679331 hasConcept C62520636 @default.
- W4362679331 hasConcept C86803240 @default.
- W4362679331 hasConceptScore W4362679331C119857082 @default.
- W4362679331 hasConceptScore W4362679331C121332964 @default.
- W4362679331 hasConceptScore W4362679331C127413603 @default.
- W4362679331 hasConceptScore W4362679331C138885662 @default.
- W4362679331 hasConceptScore W4362679331C153180895 @default.
- W4362679331 hasConceptScore W4362679331C154945302 @default.
- W4362679331 hasConceptScore W4362679331C159985019 @default.
- W4362679331 hasConceptScore W4362679331C170154142 @default.
- W4362679331 hasConceptScore W4362679331C18555067 @default.
- W4362679331 hasConceptScore W4362679331C192562407 @default.
- W4362679331 hasConceptScore W4362679331C204323151 @default.
- W4362679331 hasConceptScore W4362679331C22508944 @default.
- W4362679331 hasConceptScore W4362679331C2776401178 @default.
- W4362679331 hasConceptScore W4362679331C2778755073 @default.
- W4362679331 hasConceptScore W4362679331C31972630 @default.
- W4362679331 hasConceptScore W4362679331C41008148 @default.
- W4362679331 hasConceptScore W4362679331C41895202 @default.
- W4362679331 hasConceptScore W4362679331C59822182 @default.
- W4362679331 hasConceptScore W4362679331C62520636 @default.
- W4362679331 hasConceptScore W4362679331C86803240 @default.
- W4362679331 hasLocation W43626793311 @default.
- W4362679331 hasOpenAccess W4362679331 @default.
- W4362679331 hasPrimaryLocation W43626793311 @default.
- W4362679331 hasRelatedWork W1504288058 @default.
- W4362679331 hasRelatedWork W2048505601 @default.
- W4362679331 hasRelatedWork W2116675934 @default.
- W4362679331 hasRelatedWork W2144724818 @default.
- W4362679331 hasRelatedWork W2167293474 @default.
- W4362679331 hasRelatedWork W2331674254 @default.
- W4362679331 hasRelatedWork W2358403311 @default.
- W4362679331 hasRelatedWork W2544359817 @default.
- W4362679331 hasRelatedWork W3042897387 @default.
- W4362679331 hasRelatedWork W4310007291 @default.
- W4362679331 isParatext "false" @default.
- W4362679331 isRetracted "false" @default.
- W4362679331 workType "article" @default.