计算机技术与发展2026，Vol.36Issue(2)：46-53,86,9.DOI:10.20165/j.cnki.ISSN1673-629X.2025.0234
基于YOLO-RDHL的粮田害虫检测方法
Grain Field Pest Detection Method Based on YOLO-RDHL
摘要
Abstract
Due to challenges such as target overlap,scale variation,and complex backgrounds in natural field environments,pest detection in grain fields often suffers from insufficient perception of key features,leading to a decline in model generalization ability.To address this issue,we propose an improved YOLOv8-based detection method named YOLO-RDHL for grain field pest detection.Firstly,an enhanced HC-DySample module is introduced into the neck network to improve the model's ability to capture pest features and perceive overlapping regions.Secondly,parallel multi-branch convolution modules(DBB)are integrated into the C2F modules of both the backbone and neck networks,enhancing the model's capability to represent multi-scale features.Thirdly,an improved RAMLCA self-at-tention mechanism is embedded in the backbone network to strengthen the model's recognition of target features in complex backgrounds.Finally,the detection head is replaced with an improved LSCDv2 module,effectively increasing the detection speed.Experimental results show that compared to the original YOLOv8 model,YOLO-RDHL achieves improvements of 5.1 percentage points,4.4 percentage points,4.8 percentage points,and 1.7 percentage points in Precision,Recall,mAP50,and mAP50-95,respectively.In terms of inference time and memory usage,YOLO-RDHL exhibits no significant difference compared to YOLOv8.YOLO-RDHL significantly improves detection accuracy while maintaining low inference latency and memory usage,demonstrating a better performance balance.This study provides both theoretical and practical support for pest recognition and early warning in natural field environments.关键词
粮田害虫/YOLOv8/目标检测/害虫识别/动态上采样Key words
grain field pests/YOLOv8/object detection/pest identification/dynamic upsampling分类
信息技术与安全科学引用本文复制引用
潘巍,王涛,杜勇..基于YOLO-RDHL的粮田害虫检测方法[J].计算机技术与发展,2026,36(2):46-53,86,9.基金项目
黑龙江省自然科学基金赞助项目(LH2020C001) （LH2020C001）
黑龙江科技大学引进高层次人才科研启动基金项目(HKD202326) （HKD202326）
