关键词: 关键词: YOLOv8, 混合局部通道注意力, 内容感知特征重组, 适应性下采样模块

Abstract: Abstract: To address the low accuracy issue in tennis racket and curtain target detection within training hall scenarios, HSK-YOLOv8 was proposed as an effective algorithm that enhanced detection accuracy by expanding the feature extraction scope and optimizing path strategies. Firstly, the Mixed Local Channel Attention (MLCA) mechanism was integrated into the C2f module to achieve local-region feature fusion and cross-regional information interaction, thereby improving high-quality feature extraction capabilities and effectively resolving multi-layer feature fusion challenges. Secondly, the conventional Upsample module was replaced with Content-Aware Reassembly of Features (CARAFE), which enhanced image resolution through its advanced content-aware feature reorganization strategy, enabling finer-grained feature capture. Thirdly, convolutional layers with output channels ≥256 were substituted with the Adaptive Downsampling Module (ADown) from YOLOv9. This module mitigated gradient reduction issues through selective gradient descent in parallel network channels while improving detection precision. Finally, the Pyramid 2 Layer (P2) from YOLOv8 was implemented to provide additional deep supervision for the detection algorithm, optimizing the deep neural network architecture and enhancing training efficiency. Experimental results demonstrated that compared with the baseline model YOLOv8n, HSK-YOLOv8 achieved a 4.5 improvement in GFLOPs, a 1.5% increase in recall rate, a 1.2% enhancement in mAP50, and a 1.1% elevation in mAP50-95. The proposed model also showed superior accuracy compared with other mainstream detection models.

Key words: Keywords: YOLOv8, mixed local channel attention, content sensing feature recombination, adaptive subsampling module