Motion blur caused by camera shake produces ghosting artifacts that substantially degrade edge side object detection. Existing approaches either suppress blur as noise and lose discriminative structure, or apply full image restoration that increases latency and limits deployment on resource constrained devices. We propose DFRCP, a Dynamic Fuzzy Robust Convolutional Pyramid, as a plug in upgrade to YOLOv11 for blur robust detection. DFRCP enhances the YOLOv11 feature pyramid by combining large scale and medium scale features while preserving native representations, and by introducing Dynamic Robust Switch units that adaptively inject fuzzy features to strengthen global perception under jitter. Fuzzy features are synthesized by rotating and nonlinearly interpolating multiscale features, then merged through a transparency convolution that learns a content adaptive trade off between original and fuzzy cues. We further develop a CUDA parallel rotation and interpolation kernel that avoids boundary overflow and delivers more than 400 times speedup, making the design practical for edge deployment. We train with paired supervision on a private wheat pest damage dataset of about 3,500 images, augmented threefold using two blur regimes, uniform image wide motion blur and bounding box confined rotational blur. On blurred test sets, YOLOv11 with DFRCP achieves about 10.4 percent higher accuracy than the YOLOv11 baseline with only a modest training time overhead, reducing the need for manual filtering after data collection.

翻译：相机抖动引起的运动模糊会产生重影伪影，显著降低边缘侧目标检测性能。现有方法要么将模糊作为噪声抑制而丢失判别性结构，要么采用全图像恢复增加延迟并限制其在资源受限设备上的部署。我们提出DFRCP（动态模糊鲁棒卷积金字塔）作为YOLOv11的插件式升级方案，以实现模糊鲁棒检测。DFRCP通过结合大尺度与中尺度特征并保留原始表征来增强YOLOv11特征金字塔，同时引入动态鲁棒切换单元，自适应注入模糊特征以增强抖动条件下的全局感知能力。模糊特征通过多尺度特征的旋转和非线性插值合成，再通过透明度卷积进行融合，该卷积学习原始特征与模糊特征之间的内容自适应权衡。我们进一步开发了CUDA并行旋转与插值内核，避免边界溢出并实现400倍以上的加速，使该设计适用于边缘部署。我们在包含约3,500张图像的私有小麦病虫害数据集上进行配对监督训练，并使用两种模糊机制（均匀图像级运动模糊和边界框约束的旋转模糊）将数据增强三倍。在模糊测试集上，搭载DFRCP的YOLOv11相比基线YOLOv11准确率提升约10.4%，仅带来适度的训练时间开销，减少了数据采集后人工筛选的需求。