基于CBAM-YOLO v7的自然环境下棉叶病虫害识别方法

Identification Method of Cotton Leaf Pests and Diseases in Natural Environment Based on CBAM-YOLO v7

针对自然环境下棉花叶片病害检测难度大和人工设计特征提取器难以获取与棉叶病虫害相近特征表达的问题,提出一种改进的注意力机制YOLO v7算法(CBAM-YOLO v7)。该模型在YOLO v7模型基础上,在Backbone与Head中间增加注意力机制CBAM,并在Head部进行4倍下采样,然后将CBAM-YOLO v7模型用于棉叶病虫害识别,并与YOLO v5和YOLO v7进行对比试验。试验结果表明:蚜虫和正常叶片检测方面,YOLO v7可取得好的检测结果;CBAM-YOLO v7对黄萎病、棉盲蝽、红蜘蛛棉叶病虫害图像检测的准确率高于其他模型。CBAM-YOLO v7的mAP为85.5%,相较于YOLO v5提高21个百分点,相较于YOLO v7提高4.9个百分点;单幅图检测耗时为29.26 ms,可为棉叶病害在线监测提供理论基础。

To address the challenges of detecting cotton leaf diseases in natural environments and the difficulty of manually designing feature extractors that capture similar feature expressions as those of cotton leaf diseases,an improved attention mechanism YOLO v7 algorithm(CBAM-YOLO v7)was proposed.Building upon the YOLO v7 model,the approach integrated the convolutional block attention module(CBAM)into the backbone and head of the model and incorporated a four times downsampling step within the head.The CBAM-YOLO v7 model was employed for the identification of cotton leaf diseases in Southern Xinjiang,and comparative experiments were conducted against YOLO v5 and YOLO v7.Experimental results revealed that in terms of aphid and normal leaf detection,YOLO v7 achieved favorable detection outcomes.Notably,CBAM-YOLO v7 demonstrated higher accuracy in detecting diseases like Fusarium wilt,cotton mirid bugs,and red spider mites when compared with other models.CBAM-YOLO v7 achieved a mean average precision(mAP)of 85.5%,representing a 21 percentage points increase over YOLO v5 and a 4.9 percentage points increase over YOLO v7.Moreover,the detection time for a single image was 29.26 ms,offering a theoretical foundation for online monitoring of cotton leaf diseases.