In this study, we examine the associations between channel features and convolutional kernels during the processes of feature purification and gradient backpropagation, with a focus on the forward and backward propagation within the network. Consequently, we propose a method called Dense Channel Compression for Feature Spatial Solidification. Drawing upon the central concept of this method, we introduce two innovative modules for backbone and head networks: the Dense Channel Compression for Feature Spatial Solidification Structure (DCFS) and the Asymmetric Multi-Level Compression Decoupled Head (ADH). When integrated into the YOLOv5 model, these two modules demonstrate exceptional performance, resulting in a modified model referred to as YOLOCS. Evaluated on the MSCOCO dataset, the large, medium, and small YOLOCS models yield AP of 50.1%, 47.6%, and 42.5%, respectively. Maintaining inference speeds remarkably similar to those of the YOLOv5 model, the large, medium, and small YOLOCS models surpass the YOLOv5 model's AP by 1.1%, 2.3%, and 5.2%, respectively.

翻译：本研究探讨了在特征提纯和梯度反向传播过程中，通道特征与卷积核之间的关联机制，重点关注网络中的前向传播与反向传播过程。基于此，我们提出了一种名为“密集通道压缩的特征空间固化”的方法。以此方法的核心思想为依据，我们设计了两种创新的骨干网络和头部网络模块：密集通道压缩特征空间固化结构（DCFS）和非对称多级压缩解耦头部（ADH）。当将这两个模块集成到YOLOv5模型中时，它们表现出了卓越的性能，由此产生的改进模型被称为YOLOCS。在MSCOCO数据集上的评估结果显示，YOLOCS的大、中、小型模型分别获得了50.1%、47.6%和42.5%的平均精度（AP）。在推理速度与YOLOv5模型保持高度相似的情况下，YOLOCS的大、中、小型模型在AP上分别超越了YOLOv5模型1.1%、2.3%和5.2%。