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 horiz...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.展开更多
文摘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.
