中圖分類號：TP391 文獻(xiàn)標(biāo)志碼：A DOI: 10.16157/j.issn.0258-7998.244871 中文引用格式： 朱宇浩，童孟軍. 一種改進(jìn)的基于Mask R-CNN的玉米大斑病實例分割算法[J]. 電子技術(shù)應(yīng)用，2024，50(5)：71-76. 英文引用格式： Zhu Yuhao，Tong Mengjun. An improved Mask R-CNN based instance segmentation algorithm for maize Northern Leaf Blight[J]. Application of Electronic Technique，2024，50(5)：71-76.
An improved Mask R-CNN based instance segmentation algorithm for maize Northern Leaf Blight
Zhu Yuhao1，Tong Mengjun1，2
1.College of Mathematics and Computer Science， Zhejiang A&F University；2.Zhejiang Provincial Key Laboratory of Forestry Intelligent Monitoring and Information Technology Research
Abstract： Maize, a crucial staple crop in China, is frequently beset by production challenges stemming from diseases such as maize Northern Leaf Blight, Southern Corn Leaf Blight, and rust, along with insect pests. These maladies significantly undermine maize yield and quality, presenting a potential menace to agricultural production stability. In recent times, visual disease detection techniques have emerged as pivotal instruments for disease management, offering heightened precision relative to conventional methods. This paper leverages the Mask R-CNN architecture as its foundation, integrating DyHead, Groie, and OHEM modules to augment the model's proficiency in segmenting images containing minute disease manifestations. The enhanced Mask R-CNN model exhibits outstanding performance in disease image segmentation, witnessing a 4% uplift in mean average precision (mAP) and an 8.5% enhancement in accuracy for small object segmentation. Compared to analogous instance segmentation models like YOLOv5 and YOLACT++, this model displays superior prowess. To substantiate the utility of each incorporated module, ablation studies were carried out, revealing their constructive roles. Thus, this methodology furnishes a sturdy theoretical underpinning and technological means for the efficacious and precise detection of maize Northern Leaf Blight.
Key words : instance segmentation；Northern Leaf Blight；Mask R-CNN；computer vision；attention mechanism
