Research on multi class pests identification and detection based on fusion attention mechanism with Mask-RCNN-CBAM

Research on multi class pests identification and detection based on fusion attention mechanism with Mask-RCNN-CBAM

This study addresses challenges in agricultural pest detection, such as false positives and missed detections in complex environments, by proposing an enhanced Mask-RCNN model integrated with a Convolutional Block Attention Module (CBAM). The framework combines three innovations: (1) a CBAM attentio...

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Bibliographic Details
Main Authors: Xingwang Wang, Can Hu, Xufeng Wang, Hainie Zha, Xueyong Chen, Shanshan Yuan, Jing Zhang, Jianfeng Liao, Zhangying Ye
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-05-01
Series:Frontiers in Agronomy
Subjects:
Mask-RCNN-CBAM
attention mechanism
feature enhanced pyramid network
dual channel downsampling
pest extraction
deep learning
Online Access:https://www.frontiersin.org/articles/10.3389/fagro.2025.1578412/full
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https://www.frontiersin.org/articles/10.3389/fagro.2025.1578412/full

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