基于改进YOLOF模型的田间农作物害虫检测方法

Insect Pest Detection of Field Crops Based on Improved YOLOF Model

田间害虫图像数据采集困难,并且传统的检测模型大多使用复杂的特征金字塔(Feature pyramid network,FPN)结构提升精度,这在一定程度上影响了检测的实时性。为此,本研究通过设计诱虫灯装置构建害虫数据集FieldPest5,并且对无FPN结构的检测器YOLOF进行改进,提出兼顾检测精度和效率的害虫检测模型YOLOF_PD。首先,增加Cutout数据增强方法缓解害虫图像中的遮挡问题,并且使用CIoU损失函数获得更好的框回归位置;其次,在原有坐标注意力机制(Coordinate attention,CA)的全局平均池化(Global average pooling,GAP)路径中增加全局最大池化(Global max pooling,GMP)路径,并且使用可学习参数自适应更新不同路径的权重,提出自适应坐标注意力机制(Adaptive coordinate attention,ACA),增强模型的信息表征能力;最后,对YOLOF膨胀编码器中的Projector和Residual模块进行改进,在Projector模块的3×3卷积后引入ACA注意力机制,在Residual模块中融合3×3的深度可分离卷积和1×1的逐点卷积,提出Dilated_Dwise_ACA编码器,提高YOLOF对小尺度害虫的检测性能。实验结果表明:改进后的YOLOF_PD模型在FieldPest5测试集上的平均精度均值(Mean average precision,mAP)为93.7%,较改进前提升2.1个百分点,并且检测时图像传输速率为42.4 f/s,能够满足害虫快速检测的要求。对比Cascade R CNN、RetinaNet、ATSS等模型,YOLOF_PD模型在检测效果和检测速度方面均取得了良好性能。

The wide distribution of pests in the field leads to difficulties in image data acquisition,and most of the traditional detection models use complex feature pyramid network(FPN)to enhance detection accuracy,which affects the real⁃time detection to some extent.To this end,the trap lamp device was designed to construct the pest dataset FieldPest5 and the detector YOLOF,which does not use the FPN structure,was improved to propose a pest detection model YOLOF_PD that balanced detection accuracy and efficiency.Firstly,the Cutout data augmentation method was added to alleviate the occlusion problem in the pest images,and the complete intersection over union(CIoU)loss function was used to obtain better box regression positions.Secondly,the adaptive coordinate attention(ACA)mechanism was proposed to enhance the information representation capability of the model.Specifically,the global maximum pooling(GMP)path was added to the global average pooling(GAP)path of the original coordinate attention(CA)mechanism,and the weights of different paths were updated adaptively by using learnable parameters.Finally,the Dilated_Dwise_ACA encoder was proposed to improve the performance of YOLOF for small⁃scale pest detection.Improvements were made to the projector and residual modules in the dilated encoder.The ACA attention mechanism was introduced after the 3×3 convolution in the projector module,and in the Residual module 3×3 depth⁃separable convolution and 1×1 pointwise convolution were fused.The experimental results showed that the improved YOLOF_PD model mAP achieved 93.7%on the FieldPest5 test set,which was 2.1 percentage points higher than that of the model before improvement,and the detection speed was 42.4 f/s,which can meet the requirements of fast pest detection.Compared with Cascade R CNN,RetinaNet and ATSS,YOLOF_PD achieved good performance in terms of detection effect and detection speed.The research result can lay a solid foundation for field pest data collection as well as real⁃time pest detection.