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