基于YOLO-RDHL的粮田害虫检测方法OA

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.