针对多种农作物病虫害图像, 在自然环境下因虫害种类繁多, 小目标特征相似的技术问题, 导致检测困难难以达到令人满意的精度. 本文提出了一种自然背景下加强局部特征和全局特征信息融合的害虫检测识别模型YOLOv5-EB, 在公开的大规模害虫数据集IP102上进行实验, 结果表明该研究比YOLOv5模型精确度提高了5个百分点. 引入一维卷积替换CBAM中通道注意力的MLP操作, 优化了通道注意力经过全局处理后容易忽略通道内信息交互的问题; 其次使用6×6卷积替换Focus操作, 来增强提取害虫特征的能力. 实验结果表明, 对害虫进行检测时, YOLOv5-EB的平均精度值达到了87%, 与Faster R-CNN、EfficientDet、YOLOv3、YOLOv4、YOLOv5模型相比, 不仅有效提高了作物害虫图像的识别性能, 而且有效提高了检测速度. 研究表明, YOLOv5-EB算法满足对多种农作物病虫害目标检测的准确性和实时性要求.
For a variety of crop disease and pest images, it is difficult to achieve satisfactory accuracy due to the technical problems of various diseases and pests and similar characteristics of small targets in the natural environment. In this study, a pest detection and identification model, namely YOLOv5-EB that enhances the fusion of local feature and global feature information in the natural background is proposed, and experiments are carried out on the published large-scale pest dataset IP102. The results show that the accuracy of this study is improved by five percentage points compared with the YOLOv5 model. The MLP operation of replacing channel attention in CBAM with one-dimensional convolution is introduced, which optimizes the problem that channel attention is easy to ignore the information interaction in the channel after global processing. Secondly, the Focus operation is replaced by 6×6 convolution to enhance the ability to extract pest features. The experimental results show that the average accuracy of YOLOv5-EB reaches 87% in detecting pests, which not only effectively improves the identification performance of crop pest images but also increases the detection speed compared with Faster R-CNN, EfficientDet, YOLOv3, YOLOv4, and YOLOv5 models. The study reveals that the YOLOv5-EB algorithm meets the accuracy and real-time requirements of target detection of various crop diseases and pests.