用辅助任务提升货架缺货检测准确率
OOS-DSD: Improving Out-of-stock Detection in Retail Images using Auxiliary Tasks
- 在YOLOv8基础上增加分割与深度估计分支，多任务协同
- 相比现有方法提升1.8% mAP，深度归一化使性能再增4.2%
- 适合零售自动化、智能巡检场景的开发者使用
缺货检测是零售业中识别商品是否缺失的重要环节。本文提出OOS-DSD，一种基于深度学习的新型缺货检测方法，通过引入辅助任务实现性能提升。具体地，我们在成熟的YOLOv8目标检测架构上增加卷积分支，同时完成缺货检测、商品分割和场景深度估计。其中，缺货检测与分割分支使用真实标注数据训练，深度估计分支则利用SOTA深度估计模型Depth Anything V2生成的伪标签进行训练。由于伪标签提供的是相对深度信息，我们设计了一种有效的深度归一化策略以稳定训练过程。实验结果表明，该方法在mAP上超越现有SOTA方法1.8个百分点；消融实验进一步验证了辅助学习（提升3.7% mAP）和深度归一化（提升4.2% mAP）的有效性。
原文摘要 · Abstract (English)
Out-of-stock (OOS) detection is a very important retail verification process that aims to infer the unavailability of products in their designated areas on the shelf. In this paper, we introduce OOS-DSD, a novel deep learning-based method that advances OOS detection through auxiliary learning. In particular, we extend a well-established YOLOv8 object detection architecture with additional convolutional branches to simultaneously detect OOS, segment products, and estimate scene depth. While OOS detection and product segmentation branches are trained using ground truth data, the depth estimation branch is trained using pseudo-labeled annotations produced by the state-of-the-art (SOTA) depth estimation model Depth Anything V2. Furthermore, since the aforementioned pseudo-labeled depth estimates display relative depth, we propose an appropriate depth normalization procedure that stabilizes the training process. The experimental results show that the proposed method surpassed the performance of the SOTA OOS detection methods by 1.8% of the mean average precision (mAP). In addition, ablation studies confirm the effectiveness of auxiliary learning and the proposed depth normalization procedure, with the former increasing mAP by 3.7% and the latter by 4.2%.
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