关键词: 枸杞虫害, 目标检测, YOLOv11, 协同语义特征增强, 轻量化

Abstract:

[Objective] Goji berry (Lycium barbarum L.) is an important medicinal and edible crop with considerable nutritional, health-care, and economic value. However, pest detection in complex field environments remains challenging because pests exhibit small target sizes, weak fine-grained features, partial occlusion, and strong background interference. These factors can cause incomplete target features and inaccurate localization and classification. Therefore, an object detection model that enhances fine-grained features, strengthens cross-scale semantic information transmission, and maintains low computational complexity is needed for accurate and efficient goji berry pest detection. [Methods] Based on the you only look once (YOLO) framework, a collaborative semantic feature enhancement YOLOv11 (CSFE-YOLOv11) model was proposed. The model performs collaborative optimization at three successive stages: fine-grained feature enhancement, cross-scale semantic fusion, and lightweight prediction optimization. First, an attention-embedded feature enhancement module (AFE) was introduced into the backbone to enhance local fine-grained pest features and suppress irrelevant background responses by combining local convolution-based feature extraction with global attention-based contextual modeling. Second, a cross-scale semantic fusion module (CSF) was constructed in the neck to strengthen nonlinear interactions and semantic information transmission among different scales through a depthwise convolution branch (DWB), a dual-branch nonlinear interaction structure (SC), and a channel mapping and reconstruction component (SSA). Finally, a lightweight decoupled prediction head (LDP head) was designed to separate classification and localization tasks and reduce redundant computation. A 17-class goji berry pest dataset was used for training and evaluation. Small, medium, and large targets accounted for 4.01%, 24.09%, and 71.90%, respectively, while targets with different degrees of occlusion accounted for 38.41%. Comparative experiments, ablation studies, multi-scale analysis, visualization, cross-dataset evaluation, and local field validation were conducted. [Results and Discussion] On the 17-class goji berry pest dataset, CSFE-YOLOv11 achieved a precision of 85.27%, recall of 71.49%, mean Average Precision at IoU=0.5 (mAP50) of 75.29%, and mAP50-95 of 48.25%. Compared with YOLOv11, mAP50 and mAP50:95 increased by 4.47 and 2.36 percentage points, respectively. The number of parameters decreased from 2.59 M to 2.28 M, while GFLOPs decreased from 6.3 to 5.2, corresponding to reductions of 11.97% and 17.46%, respectively. Ablation experiments showed complementary effects among AFE, CSF, and the LDP head. The average precision (AP) of small, medium, and large targets increased from 1.6%, 18.2%, and 50.4% to 5.4%, 32.5%, and 52.8%, respectively. Visualization results showed that the proposed model improved the detection of small, weakly salient, and partially occluded pests while reducing responses to complex background regions. On the IP102 dataset, three independent experiments with random seeds of 42 123, and 456 were conducted. CSFE-YOLOv11 achieved an mAP50 of 63.17%±0.29%, compared with 61.07%±0.68% for YOLOv11, demonstrating improved cross-dataset performance with relatively stable results. Local Ningxia field validation further showed a 4-percentage-point improvement in mAP50 over YOLOv11. [Conclusion] CSFE-YOLOv11 provides a collaborative detection framework integrating fine-grained feature enhancement, cross-scale semantic fusion, and lightweight decoupled prediction. By assigning complementary functions to AFE, CSF, and LDP head at different stages of the detection network, the proposed model improves the representation and prediction of difficult pest targets while reducing model parameters and computational complexity. The results indicate its potential for accurate and efficient goji berry pest monitoring in complex field environments and provide a feasible technical reference for subsequent lightweight deployment in smart agriculture.

Key words: goji berry pest detection, object detection, YOLOv11, collaborative semantic feature enhancement, lightweight