山东农业科学2025，Vol.57Issue(10)：149-157,9.DOI:10.14083/j.issn.1001-4942.2025.10.019
面向边缘设备的改进YOLOv7-tiny线虫检测模型
Improved YOLOv7-tiny Nematode Detection Model for Edge Devices
摘要
Abstract
Nematodes are widely used as model organisms in biological researches.To address the chal-lenges during nematode activity screening stage,such as the small size of individual nematode target,easy to be obscured,and the poor lightweight performance and difficult to deploy on edge devices of existing nematode detection models,we proposed an improved YOLOv7-tiny nematode detection model tailored for edge devices.The MobileOne network was employed as the backbone network to boost the model's computational efficiency.The Generalized Feature Pyramid Network(GFPN)was incorporated to refine the Neck layer to enable adap-tive fusion of"skip-layer"and"cross-scale"approaches,thereby enriching the representation of image fea-tures.Additionally,a dual-layer routing attention mechanism(BRA)was introduced into the Neck layer to enhance the feature extraction capability for obscured targets.The fourth detection head was added into the Head layer to enhance the detection capability for small targets.The INT8 quantization processing was adopted for the model using the perceptual quantization method,with an asymmetric quantization strategy applied to the activation values to further reduce computational load and achieve model lightweighting.The improved model was deployed and tested on the edge device Jetson Nano.The experimental results indicated that compared to the original model,the improved model showed an increase in mean average precision(mAP@0.5)by 2.7 percentage points,a reduction in computational demand(GFLOPs)by 67.71％,and an increase in detection frame rate(FPS)by 23.01％.These results demonstrated that the accuracy of the improved model was signifi-cantly enhanced,and it could be enabled rapid and precise detection of nematode targets on edge devices.关键词
边缘设备/线虫检测/YOLOv7-tiny/轻量化Key words
Edge device/Nematode detection/YOLOv7-tiny/Lightweight分类
农业科学引用本文复制引用
李耀东,侯文进,侯华鑫,王秀丽,王东,曲建平,周波,刘璋..面向边缘设备的改进YOLOv7-tiny线虫检测模型[J].山东农业科学,2025,57(10):149-157,9.基金项目
山东省重大科技创新工程项目(2019JZZY010716) （2019JZZY010716）
山东农业大学横向科研项目(233024) （233024）
山东省大学生创新创业训练计划项目(S202310434217) （S202310434217）
