【摘要】 本研究针对传统棉花颜色级检测存在的精度低、效率低、一致性弱等核心瓶颈，融合计算机视觉与深度学习技术，构建“数据采集—模型训练—性能验证—在线应用”的端到端智能检验系统。研究通过4800万像素工业相机与标准光源搭建标准化采集方案，结合网格线引导采集、可视化标注功能，建成含数千张高分辨率图像的细粒度棉花样本库（CottonDataSet）；创新提出改进Swin-RTMDet架构——以Swin-Base Transformer为骨干网络（stage3堆叠18个模块，下采样率16倍），引入跨阶段特征增强模块（CSFE）与通道注意力机制强化细粒度特征提取，搭配RTMDet优化检测头提升定位效率；采用两阶段数据增强（前期Mosaic/MixUp强增强、后期精调）、DynamicSoftLabelAssigner动态标签分配、QFL+DIoU Loss联合损失函数等策略优化模型,最终集成的智能检验系统，在配备NVIDIA GeForce RTX 2080的硬件平台上，实测综合准确率达98.48%，单图处理时间低至0.419秒，支持10类棉花等级识别，有效满足工业化实时在线检测需求，为棉花产业品质分级智能化提供技术支撑。更多还原

【Abstract】 Aiming at the core bottlenecks of low accuracy, low efficiency and weak consistency in traditional cotton color grade detection(strong subjectivity of manual sensory inspection and poor adaptability of HVI instrument detection), this study integrates computer vision and deep learning technologies to construct an end-to-end intelligent inspection system covering "data collection-model training-performance verification-online application". In the research, a standardized acquisition scheme was built using a 48-megapixel industrial camera and a standard light source, and combined with grid-guided acquisition and visual annotation functions, a fine-grained cotton sample library(CottonDataSet) containing thousands of high-resolution images was established. An improved Swin-RTMDet architecture was innovatively proposed: with Swin-Base Transformer as the backbone network(18 modules stacked in stage 3, downsampling rate of 16x), a cross-stage feature enhancement module(CSFE) and channel attention mechanism were introduced to strengthen the extraction of fine-grained features, and an optimized RTMDet detection head was matched to improve positioning efficiency. Strategies such as two-stage data augmentation(early-stage strong augmentation with Mosaic/MixUp, late-stage fine-tuning), DynamicSoftLabelAssigner dynamic label assignment, and QFL+DIoU Loss joint loss function were adopted to optimize the model. Finally, the integrated intelligent inspection system, on a hardware platform equipped with NVIDIA GeForce RTX 2080, achieved a measured comprehensive accuracy of 98.48% and a single-image processing time as low as 0.419 seconds, supporting the recognition of 10 cotton grades. It effectively meets the requirements of industrial real-time online detection and provides technical support for the intelligent upgrading of cotton industry quality grading.更多还原