GIWT-YOLO: an efficient multi-scale framework for real-time Scolytinae pests detection.

ABSTRACT: The broad range of Scolytinae pests sizes and their subtle visual similarities, especially in smaller species, continue to challenge the accuracy of mainstream object detection models. To address these challenges, this paper proposes GIWT-YOLO, a lightweight detection model based on YOLOv11s, specifically tailored for Scolytinae pests detection. (1) We designed a lightweight multi-scale convolution module, GIConv, to improve the model's ability to extract features at different pest scales. This module enhances the accuracy of small-object detection while reducing the computational cost and parameter complexity of the backbone. (2) The WTConv module inspired by wavelet transform is introduced into the backbone. This enlarges the effective receptive field and improves the model's ability to

SUBMITTER: Liu J 

PROVIDER: S-EPMC12511143 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature