基于改进YOLO v8s-OBB的黄瓜霜霉病菌分生孢子梗与孢子囊定量检测

Quantitative Detection of Conidiophores and Sporangium of Cucumber Downy Mildew Based on Improved YOLO v8s-OBB

摘要:

黄瓜霜霉病作为一种严重威胁黄瓜生产和品质的真菌性病害，其分生孢子梗和孢子囊的定量检测对病害防治关口前移具有重要意义，由于分生孢子梗及孢子囊存在形态多样和方向不同特征，传统的水平边界框检测方法无法准确检测。因此，本文提出了一种基于改进YOLO v8s-OBB的检测方法，通过引入卷积块注意力机制（Convolutional block attention module, CBAM）和轻量级共享卷积检测头（Lightweight shared convolutional detection head, LSCD）模块，旨在提高黄瓜霜霉病菌分生孢子梗及孢子囊的检测效率和准确性。通过引入CBAM，增强了模型对关键特征的识别能力，使其更聚焦于显微图像中的重要区域，进而提高了对小目标的检测能力。LSCD通过共享卷积操作集成多尺度特征，提高了模型对不同尺寸目标的检测性能，同时降低了计算成本，以适应资源受限环境的需求。旋转边界框技术能准确捕捉分生孢子梗及孢子囊的倾斜和旋转姿态。实验结果表明，对比原始YOLO v8s-OBB模型，改进YOLO v8s-OBB模型不仅模型尺寸进一步减少，而且对黄瓜霜霉病菌分生孢子梗及孢子囊的检测性能更优，其精确率、召回率和mAP@0.5分别达到96.0%、90.1%和96.5%。同时，改进YOLO v8s-OBB模型检测精度均优于S2ANet、H2RBox和R2CNN等先进旋转目标检测模型。此研究验证了改进模型在实际应用中的有效性，并为黄瓜霜霉病的早期诊断提供了技术支持。

Abstract:

Cucumber downy mildew is a fungal disease that severely threatens cucumber production and quality. Quantitative detection of sporangia and conidiophores is crucial for early disease prevention. However, traditional horizontal bounding box detection methods cannot accurately detect these features due to their diverse morphology and orientations. Therefore, an improved YOLO v8sOBB detection method was proposed by introducing the convolutional block attention module (CBAM) and the lightweight shared convolution detection head (LSCD) module. The aim was to enhance the detection efficiency and accuracy of sporangia and conidiophores of cucumber downy mildew. By incorporating CBAM, the model’s ability to identify key features was enhanced, allowing it to focus more on critical regions in microscopic images and improve the detection of small targets. The LSCD integrated multi-scale features through shared convolution operations, enhancing the model’s detection performance for targets of different sizes while reducing computational costs, making it suitable for resource-constrained environments. The rotated bounding box technique accurately captured sporangia and conidiophores’ inclination and rotation postures. Experimental results showed that, compared with the original YOLO v8s-OBB model, the improved YOLO v8s-OBB model not only reduced the model size but also achieved superior detection performance for sporangia and conidiophores of cucumber downy mildew, with precision, recall, and mAP@0.5 reaching 96.0%, 90.1%, and 96.5%, respectively. The improved YOLO v8s-OBB model outperformed advanced rotated object detection models such as S2ANet, H2RBox, and R2CNN in detection accuracy. The research result can validate the effectiveness of the improved model in practical applications and provide technical support for the early diagnosis of cucumber downy mildew.