用可变形空间变换提升农田目标检测精度
CBAM-STN-TPS-YOLO: Enhancing Agricultural Object Detection through Spatially Adaptive Attention Mechanisms
- 引入TPS替代传统仿射变换，实现非刚性形变的灵活对齐
- 在PGP数据集上误检率降低12%，mAP显著提升
- 轻量设计适合边缘设备，适用于智慧农业实时监测
目标检测在精准农业中对植株监测、病害识别和产量估算至关重要。然而，YOLO等模型在遮挡、不规则结构和背景噪声下表现不佳。虽然空间变换网络（STN）通过学习变换增强空间不变性，但仿射映射难以处理弯曲叶片、重叠等非刚性形变。本文提出CBAM-STN-TPS-YOLO，将薄板样条（TPS）引入STN，实现更灵活的非刚性空间变换以更好对齐特征。同时结合卷积块注意力模块（CBAM），抑制背景噪声并强化关键空间与通道特征。在遮挡严重的植物生长与表型（PGP）数据集上，该模型在精确率、召回率和mAP上均优于STN-YOLO，误检率降低12%。还分析了TPS正则化参数对变换平滑性与检测性能的平衡影响。该轻量级模型提升了空间感知能力，支持实时边缘部署，适用于需要高精度与高效性的智慧农业场景。
原文摘要 · Abstract (English)
Object detection is vital in precision agriculture for plant monitoring, disease detection, and yield estimation. However, models like YOLO struggle with occlusions, irregular structures, and background noise, reducing detection accuracy. While Spatial Transformer Networks (STNs) improve spatial invariance through learned transformations, affine mappings are insufficient for non-rigid deformations such as bent leaves and overlaps. We propose CBAM-STN-TPS-YOLO, a model integrating Thin-Plate Splines (TPS) into STNs for flexible, non-rigid spatial transformations that better align features. Performance is further enhanced by the Convolutional Block Attention Module (CBAM), which suppresses background noise and emphasizes relevant spatial and channel-wise features. On the occlusion-heavy Plant Growth and Phenotyping (PGP) dataset, our model outperforms STN-YOLO in precision, recall, and mAP. It achieves a 12% reduction in false positives, highlighting the benefits of improved spatial flexibility and attention-guided refinement. We also examine the impact of the TPS regularization parameter in balancing transformation smoothness and detection performance. This lightweight model improves spatial awareness and supports real-time edge deployment, making it ideal for smart farming applications requiring accurate and efficient monitoring.
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