SRNet-YOLO: A model for detecting tiny and very tiny pests in cotton fields based on super-resolution reconstruction

作者：Sen Yang, Gang Zhou, Yuwei Feng, Jiang Zhang, Zhenhong Jia · 发表于：Frontiers in Plant Science · 年份：2024 · DOI：10.3389/fpls.2024.1416940 · 被引用次数：29 · 研究领域：Smart Agriculture and AI、Advanced Neural Network Applications、Remote Sensing in Agriculture

Introduction: Effective pest management is important during the natural growth phases of cotton in the wild. As cotton fields are infested with "tiny pests" (smaller than 32×32 pixels) and "very tiny pests" (smaller than 16×16 pixels) during growth, making it difficult for common object detection models to accurately detect and fail to make sound agricultural decisions. Methods: In this study, we proposed a framework for detecting "tiny pests" and "very tiny pests" in wild cotton fields, named SRNet-YOLO. SRNet-YOLO includes a YOLOv8 feature extraction module, a feature map super-resolution reconstruction module (FM-SR), and a fusion mechanism based on BiFormer attention (BiFormerAF). Specially, the FM-SR module is designed for the feature map level to recover the important feature in detail, in other words, this module reconstructs the P5 layer feature map into the size of the P3 layer. And then we designed the BiFormerAF module to fuse this reconstruct layer with the P3 layer, which greatly improves the detection performance. The purpose of the BiFormerAF module is to solve the problem of possible loss of feature after reconstruction. Additionally, to validate the performance of our method for "tiny pests" and "very tiny pests" detection in cotton fields, we have developed a large dataset, named Cotton-Yellow-Sticky-2023, which collected pests by yellow sticky traps. Results: Through comprehensive experimental verification, we demonstrate that our proposed framework achieve...