关键词: YOLOv8, 轻量化改进, 目标检测, 特征提取, 组卷积技术

Abstract: The YOLOv8n model, which employs a Convolutional Neural Network (CNN) as its feature extraction core, typically imposes significant hardware demands on embedded target detection systems, thereby hindering effective detection under low-configuration conditions. In response to such issues, a lightweight improved algorithm based on YOLOv8n is proposed. Firstly, the DualConv module, utilizing both 3×3 and 1×1 convolutional kernels, is introduced to process input feature maps concurrently, with group convolution techniques optimizing the arrangement of convolutional filters to minimize model parameters and computational load. Secondly, the Lightweight Adaptive Downsampling Module (LADM) replaces standard convolution, effectively reducing parameters and computational volume while adaptively weighting features across different channels to preserve critical feature information. Additionally, the LSKA attention mechanism is integrated to refine the feature fusion layer, bolstering its multi-scale feature extraction capabilities. Lastly, the Lightweight Parameter Sharing and Detail-Enhanced Detection Head (LPSDED) leverages parameter sharing to efficiently predict the locations and categories of variously sized and shaped targets within images, substantially decreasing computational and parametric demands. Experimental results on the combined VOC07+12 dataset demonstrate that the enhanced YOLOv8n model achieves a 43.3% reduction in total parameters and a 37% decrease in computational load, with only a marginal 2% decline in detection accuracy. This algorithm successfully realizes model lightweighting, striking an optimal balance between model accuracy and computational efficiency.

Key words: YOLOv8, lightweight improvement, target detection, feature extraction, group convolution technique