PVConv: Enhancing Depthwise Separable Convolution via Preference-Value Learning for Similar-Feature Discrimination

Abstract

Depthwise Separable Convolution (DSC) is widely used due to its significant reduction in parameters and computational cost. However, the depthwise convolution process leads to a decrease in spatial information integration, limiting the network’s expressive power. To address this, we propose a novel Preference-Value Convolution (PVConv) to enhance DSC’s expressiveness. By integrating PVConv into DSC, we introduce the Preference-Value Depthwise Separable Convolution (PVDSC) structure. We integrate both DSC and PVDSC into the YOLOv8 framework and conduct experiments on a beverage container dataset containing visually similar object categories and background interference. Results show that, with minimal increase in parameters and computational cost, introducing preference values significantly improves detection accuracy, F1 score, and attention consistency, especially at high IoU thresholds (mAP@50:95), where object localization is greatly enhanced and certain metrics even surpass complex baseline models. Overall, PVConv significantly enhances the expressiveness of DSC-based networks while maintaining low computational overhead, with promising applications.