N-EIoU-YOLOv9: A Signal-Aware Bounding Box Regression Loss for Lightweight Mobile Detection of Rice Leaf Diseases

• URL: http://arxiv.org/abs/2601.09170v1
• Date: Wed, 14 Jan 2026 05:13:36 GMT
• Title: N-EIoU-YOLOv9: A Signal-Aware Bounding Box Regression Loss for Lightweight Mobile Detection of Rice Leaf Diseases
• Authors: Dung Ta Nguyen Duc, Thanh Bui Dang, Hoang Le Minh, Tung Nguyen Viet, Huong Nguyen Thanh, Dong Trinh Cong,
• Abstract summary: We propose N EIoU YOLOv9, a lightweight detection framework based on a signal aware bounding box regression loss.<n>The proposed loss reshapes localization gradient by combining non monotonic focusing with decoupled width and height optimization.<n>This design is particularly effective for small and low contrast targets commonly observed in agricultural disease imagery.
• Score: 0.6280530476948474
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we propose N EIoU YOLOv9, a lightweight detection framework based on a signal aware bounding box regression loss derived from non monotonic gradient focusing and geometric decoupling principles, referred to as N EIoU (Non monotonic Efficient Intersection over Union). The proposed loss reshapes localization gradients by combining non monotonic focusing with decoupled width and height optimization, thereby enhancing weak regression signals for hard samples with low overlap while reducing gradient interference. This design is particularly effective for small and low contrast targets commonly observed in agricultural disease imagery. The proposed N EIoU loss is integrated into a lightweight YOLOv9t architecture and evaluated on a self collected field dataset comprising 5908 rice leaf images across four disease categories and healthy leaves. Experimental results demonstrate consistent performance gains over the standard CIoU loss, achieving a mean Average Precision of 90.3 percent, corresponding to a 4.3 percent improvement over the baseline, with improved localization accuracy under stricter evaluation criteria. For practical validation, the optimized model is deployed on an Android device using TensorFlow Lite with Float16 quantization, achieving an average inference time of 156 milliseconds per frame while maintaining accuracy. These results confirm that the proposed approach effectively balances accuracy, optimization stability, and computational efficiency for edge based agricultural monitoring systems.

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