基于IDS-YOLO的粮堆表面小麦不完善粒实时检测方法  

Real-time Detection of Imperfect Wheat Grains on Wheat Pile Surface Based on IDS-YOLO

作　　者：范嘉伟 吴兰 闫晶晶[1] FAN Jiawei;WU Lan;YAN Jingjing(College of Electrical Engineering,Henan University of Technology,Zhengzhou 450001,China;College of Electromechanical Engineering,Henan University of Technology,Zhengzhou 450001,China)

机构地区：[1]河南工业大学电气工程学院,河南郑州450001 [2]河南工业大学机电工程学院,河南郑州450001

出　　处：《食品科学》2024年第23期268-277,共10页Food Science

基　　金：河南省科技创新领军人才支持计划项目(244200510021);河南省高校科技创新团队项目(24IRTSTHN030)。

摘　　要：现阶段仓内粮堆表面的粮情检测可由智能设备协助完成。智能设备所采集的粮堆表面图片背景密集复杂、颗粒互相重叠对检测形成噪声干扰。为解决目标检测算法对不完善颗粒的高漏检率并提高模型检测速度,本研究对轻量化网络模型YOLOV4-Tiny进行优化。首先,增加小目标检测层提升高语义信息利用率,其次,嵌入基于指数思想优化的SENet注意力机制模块,由此设计增强特征提取网络提升模型在复杂背景中对不完善颗粒的特征提取能力,提高检测精度并降低漏检率。最后,以深度可分离卷积作为主干部分残差网络的特征提取方式,减少模型的参数计算量,优化模型部署并解决实时性差的问题。实验表明本研究所提出的改进算法IDS-YOLO在检测速度和检测精度之间达到了平衡,相比于其他对比算法模型的均值平均精度平均提升了6.2%;帧率值达到88.03,满足实时检测的要求,改进后模型参数量的大小仅有5.51 MB。Currently,some intelligent devices are available to assist in the detection of imperfect wheat grains.However,the background of grain surface images acquired by intelligent devices is dense and complicated with overlapping particles,causing noise interferences in the detection of imperfect wheat grains.To address the high missed detection rate of imperfect grains in target detection algorithms and to enhance the model detection speed,this study optimized the lightweight network model YOLOV4-Tiny.First,a small target detection layer was added to enhance the utilization of high semantic information.Then,the SENet attention mechanism optimized with exponential thinking was embedded to facilitated the design of an Enhanced Feature Extraction Network(Increase-FPN)in order to enhance the model’s ability to extract features of imperfect grains amidst complex backgrounds so that the detection accuracy could be improved and false negative rates reduced.At last,depthwise separable convolution was employed as the feature extraction method for the residual network of the backbone component to reduce the calculation of model parameters,optimize model deployment,and solve the issue of poor real-time performance.Experimental results demonstrated that the improved IDS-YOLO algorithm achieved a balance between detection speed and accuracy,with an average increase of 6.2%in mean average precision(mAP)when compared with other benchmark algorithms.The frames per second(FPS)value was 88.03,meeting the real-time detection requirements,and the parameter size of the improved model was only 5.51 MB.

关 键 词：实时检测 小麦不完善粒 小目标检测 储粮品质 深度学习 

分 类 号：TS210.7[轻工技术与工程—粮食、油脂及植物蛋白工程]