基于FSLYOLO v8n的玉米籽粒收获质量在线检测方法研究

Online Detection Method of Corn Kernel Quality Based on FSLYOLO v8n

玉米籽粒破碎率和含杂率是评价玉米收获质量的关键指标。针对当前玉米籽粒直收机缺少适用于复杂田间作业环境的收获质量在线检测方法的问题,提出一种适用于小目标、多数量检测目标的玉米籽粒破碎率、含杂率轻量化检测方法。首先,根据图像中完整籽粒、破碎籽粒、玉米芯和玉米叶个体数量与个体质量的关系建立数量-质量回归模型,提出了籽粒破碎率和含杂率评估方法。其次,针对籽粒及杂质大小相近,检测物数量多,检测物面积小的特点,提出一种改进的FSLYOLO v8n算法。算法通过FasterBlock模块和无参数注意力机制SimAM改进主干网络结构,并通过使用共享卷积结合Scale模块对检测头进行改进。此外,使用SlidLoss函数替代YOLO v8n的原类别分类损失函数。FSLYOLO v8n模型的mAP@50为97.46%、帧速率为186.4 f/s,与YOLO v8n相比提高6.35%和45 f/s,且网络参数量、浮点运算量分别压缩到YOLO v8n的66.50%、64.63%,模型内存占用量仅为4.0 MB,其性能优于目前常用的轻量化模型。台架试验结果表明,提出的检测方法能够精准检测玉米籽粒破碎和含杂情况,检测准确率高达95.33%和96.15%。将改进后的模型部署在Jetson TX2开发板上,配合检测装置安装到玉米联合收获机上开展田间试验,结果表明,模型能够精准区分籽粒和杂质,满足田间工作需求。

The broken rate and impurity rate of corn kernels are key indicators for evaluating the quality of corn harvest.Aiming at the demand for online detection of corn harvest quality in complex agricultural environments,a lightweight detection method for corn kernel broken rate and impurity rate suitable for small and large detection targets was proposed.Firstly,a quantity and quality regression model was established for complete kernels,broken kernels,corn cobs,and corn bracts,and an evaluation method for kernel broken rate and impurity rate was proposed.Secondly,an improved FSLYOLO v8n algorithm was proposed to address the characteristics of similar grain and impurity sizes,large number of detection objects,and small detection area.The algorithm improved the backbone network structure through FasterBlock module and small detection area and parameter free attention mechanism SimAM,and improved detection head by using shared convolution combined with scale module.In addition,the SlidLoss function was used to replace the original category classification loss function of YOLO v8n.The average accuracy of the improved FSLYOLO v8n model mAP@50 was 97.46%,FPS was 186.4 f/s,which was 6.35%and 45 f/s higher than that of YOLO v8n.The network parameters and floating-point operations were compressed to 66.50%and 64.63%of YOLO v8n,respectively.The model size was only 4.0 MB,and its performance was better than the commonly used lightweight models.The bench experiment showed that the proposed model can accurately detect the broken and impurity rate of corn kernels.The accuracy of the detection results was as high as 95.33%and 96.15%.The improved model was deployed on the Jetson TX2 development board and the device was installed on a corn combine harvester for field experiments.