关键词: 玉米叶片, 病虫害检测, 小目标检测, 轻量化模型, YOLOv11, 自适应卷积核选择, 特征重构

Abstract:

[Objective] Early detection of corn leaf diseases and pests is important for field monitoring, precision pesticide application, and yield protection. In practical field environments, early lesions, insect bodies, eggs, and spore clusters often occupy only a very small part of the image. Their visual appearance is affected by weak texture, irregular shape, and complex backgrounds, making the visual boundary between disease targets and normal leaf tissue less distinguishable. When lightweight detectors based on the YOLO series are applied, fixed convolution kernels lack flexibility for targets with large scale differences. After repeated downsampling, small objects lose representation in deep feature maps, while larger targets dominate the regression loss. These factors make it difficult to balance detection accuracy and deployment efficiency on edge devices. To improve the recognition capability for small targets under complex field conditions, LiteFocus-Net, a lightweight enhancement framework, was developed based on YOLOv11n. [Methods] The framework improved the baseline model from three closely related aspects: adaptive backbone feature extraction, deep feature detail recovery, and scale-aware regression supervision. In the backbone, an Adaptive Kernel Lightweight Block, named AKL-Block, was designed to replace part of the original feature extraction structure. Instead of computing multiple convolution branches simultaneously during inference, AKL-Block used global average pooling and a lightweight gating module to estimate the selection probabilities of three candidate depthwise separable convolution kernels with different receptive fields. A Gumbel-Softmax strategy was used during training to keep the kernel selection process differentiable, while only the kernel with the highest selection probability is activated during inference. In this way, the model could adjust its effective receptive field according to the input feature distribution without introducing the redundant computation usually caused by parallel multi-branch structures. To alleviate the loss of small-object details in deep layers, a Feature Decomposition and Reconstruction module, namely FDR, was introduced after the P5 feature layer. The deep feature was divided into a structure branch and a detail branch. The structure branch was upsampled by bilinear interpolation to preserve global semantic information, such as leaf shape, lesion distribution, and large disease regions. The detail branch used a lightweight pixel-shuffle reconstruction operation to enhance local responses related to lesion edges, insect contours, and spore textures. The reconstructed feature was then fused with the corresponding P4 feature in the neck network, allowing the model to reuse deep semantic information while selectively strengthening detail-sensitive responses. For the regression loss, a Scale-Aware Gradient Boosting Loss, termed SAGB-Loss, was constructed to increase the training contribution of small targets. The loss combined feature-level weighting and target-area-aware weighting. The feature-level term assigned larger weights to shallow layers that are more closely related to small-object detection, while the area-aware term used a continuous exponential function to smoothly enhance the regression gradients of small targets. Experiments were carried out on a field corn leaf image dataset containing 10 324 images and eight categories of disease and pest targets. The dataset was divided into training, validation, and test subsets at a ratio of 6:2:2. [Results and Discussions] LiteFocus-Net achieved 74.97% mean Average Precision (mAP) at an Intersection over Union (IoU) threshold of 0.5, with 2.52 million parameters and 5.3 GFLOPs. Compared with the YOLOv11n baseline, the average precision for small objects (AP_s) increased from 28.1% to 32.77%, giving an improvement of 4.7 percentage points, while the computational cost decreased by 15.9 percent. Ablation experiments confirmed the contribution of each component. AKL-Block reduced the overall computational cost and improved small-target accuracy, FDR further enhanced detail-sensitive features from deep layers, and SAGB-Loss increased the regression contribution of small targets without causing an obvious decline in medium- and large-object detection. Comparisons with Inception-style and selective-kernel multi-scale modules showed that AKL-Block achieved a more favorable accuracy-complexity trade-off under lightweight deployment constraints. Comparisons with full-channel pixel-shuffle reconstruction also indicated that FDR provided a practical balance between detail recovery and additional computational cost. Deployment tests on RK3588 and Jetson Orin NX further showed that LiteFocus-Net maintained real-time inference capability after INT8 quantization, which indicated its potential for field inspection and agricultural edge intelligence applications. [Conclusions] The results suggest that LiteFocus-Net improves small-target detection of corn leaf diseases and pests without relying on a larger model scale. Future work will focus on expanding cross-region, cross-variety, and cross-growth-stage samples, and on exploring region-level dynamic kernel selection, video-based continuous detection, and weakly supervised annotation strategies to improve robustness in long-term field deployment.

Key words: corn leaf, disease and pest detection, small-target detection, lightweight model, YOLOv11, adaptive kernel selection, feature reconstruction