Cheapconv: efficient parcel segmentation and detection for real-time logistics automation
摘要
Accurate parcel segmentation is critical for real-time logistics automation, particularly under complex conditions such as occlusion and dense stacking. While deep learning-based detectors have shown promise, conventional bounding box methods often fail to provide precise localization required by robotic systems. To address this, we propose CheapConv, a lightweight multi-scale convolutional operator specifically designed for efficient dense detection and segmentation in logistics scenarios. We integrate CheapConv into a re-parameterized YOLOv8 framework, called YOLOv8-cheap, which is a purely convolutional network without attention. The proposed architecture combines dense, sparse, and decomposed convolutions in a multi-branch design, enabling rich spatial feature extraction at low computational cost. Additionally, we introduce RepDS and RepHead, which leverage structural re-parameterization to improve inference-time performance without increasing complexity. Extensive experiments on the Live Stacked Carton Dataset (LSCD) and SKU-110K benchmark validate the effectiveness of our design. YOLOv8-cheap achieves notable gains in segmentation accuracy with significantly fewer FLOPs and parameters. YOLOv8n-cheap improves