用点标注实现X光违禁品检测，大幅降低人工标注成本。
I$^2$OL-Net: Intra-Inter Objectness Learning Network for Point-Supervised X-Ray Prohibited Item Detection
- 通过内外部物体性学习模块，从点标注中挖掘物体特征。
- 在4个X光数据集上表现优于现有方法，标注成本显著降低。
- 适合需要低成本标注的安检场景应用。
自动检测X光图像中的违禁物品对公共安全至关重要。然而，现有方法严重依赖耗时的框标注。为此，本文研究了基于高效点标注的X光违禁品检测，提出一种内部-外部物体性学习网络（I²OL-Net）。该网络包含两个核心模块：内部模态物体性学习（intra-OL）模块和外部模态物体性学习（inter-OL）模块。intra-OL模块设计局部聚焦高斯掩码块与全局随机高斯掩码块，协同学习X光图像中的物体性特征；inter-OL模块引入基于小波分解的对抗学习块与物体性块，有效缓解模态差异，将自然图像中带框标注学习到的物体性知识迁移至X光图像。在此基础上，I²OL-Net显著缓解了因X光图像类内差异大导致的部分主导问题。在四个X光数据集上的实验表明，I²OL-Net在大幅降低标注成本的同时达到优异性能，提升了实用性与可推广性。
原文摘要 · Abstract (English)
Automatic detection of prohibited items in X-ray images plays a crucial role in public security. However, existing methods rely heavily on labor-intensive box annotations. To address this, we investigate X-ray prohibited item detection under labor-efficient point supervision and develop an intra-inter objectness learning network (I$^2$OL-Net). I$^2$OL-Net consists of two key modules: an intra-modality objectness learning (intra-OL) module and an inter-modality objectness learning (inter-OL) module. The intra-OL module designs a local focus Gaussian masking block and a global random Gaussian masking block to collaboratively learn the objectness in X-ray images. Meanwhile, the inter-OL module introduces the wavelet decomposition-based adversarial learning block and the objectness block, effectively reducing the modality discrepancy and transferring the objectness knowledge learned from natural images with box annotations to X-ray images. Based on the above, I$^2$OL-Net greatly alleviates the problem of part domination caused by severe intra-class variations in X-ray images. Experimental results on four X-ray datasets show that I$^2$OL-Net can achieve superior performance with a significant reduction of annotation cost, thus enhancing its accessibility and practicality.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。