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

• URL: http://arxiv.org/abs/2305.04170v6
• Date: Sun, 13 Oct 2024 12:58:36 GMT
• Title: YOLOCS: Object Detection based on Dense Channel Compression for Feature Spatial Solidification
• Authors: Lin Huang, Weisheng Li, Yujuan Tan, Linlin Shen, Jing Yu, Haojie Fu,
• Abstract summary: 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.
• Score: 38.49525419649799
• License:
• Abstract: 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.

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