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In this study, we examine the associations between channel features andconvolutional kernels during the processes of feature purification and gradientbackpropagation, with a focus on the forward and backward propagation withinthe network. Consequently, we propose a method called Dense Channel Compressionfor Feature Spatial Solidification. Drawing upon the central concept of thismethod, we introduce two innovative modules for backbone and head networks: theDense Channel Compression for Feature Spatial Solidification Structure (DCFS)and the Asymmetric Multi-Level Compression Decoupled Head (ADH). Whenintegrated into the YOLOv5 model, these two modules demonstrate exceptionalperformance, resulting in a modified model referred to as YOLOCS. Evaluated onthe MSCOCO dataset, the large, medium, and small YOLOCS models yield AP of50.1%, 47.6%, and 42.5%, respectively. Maintaining inference speeds remarkablysimilar to those of the YOLOv5 model, the large, medium, and small YOLOCSmodels surpass the YOLOv5 model's AP by 1.1%, 2.3%, and 5.2%, respectively.

YOLOCS: Object Detection based on Dense Channel Compression for Feature Spatial Solidification