CEMLB-YOLO: Efficient Detection Model of Maize Leaf Blight in Complex Field Environments

Abstract

Northern corn leaf blight is a severe fungal disease that adversely affects the health of maize crops. In order to prevent maize yield decline caused by leaf blight, we propose the YOLOv5-based object detection lightweight models to rapidly detect maize leaf blight disease in complex scenarios. Firstly, the Crucial Information Position Attention Mechanism (CIPAM) enables the model to focus on retaining critical information during downsampling to reduce information loss. We introduce the Feature Restructuring and Fusion Module (FRAFM) to extract deep semantic information and make the feature map fusion across maps at different scales more effective. Thirdly, we add the Mobile Bi-Level Transformer (MobileBit) to the feature extraction network to help the model understand complex scenes more effectively and cost-effectively. The experimental results demonstrate that the proposed model achieves 87.5% mAP@0.5 accuracy on the NLB dataset, which is 5.4% higher than the original model.