Reliable foreign-object anomaly detection and pixel-level localization in conveyor-belt coal scenes are essential for safe and intelligent mining operations. This task is particularly challenging due to the highly unstructured environment: coal and gangue are randomly piled, backgrounds are complex and variable, and foreign objects often exhibit low contrast, deformation, occlusion, resulting in coupling with their surroundings. These characteristics weaken the stability and regularity assumptions that many anomaly detection methods rely on in structured industrial settings, leading to notable performance degradation. To support evaluation and comparison in this setting, we construct \textbf{CoalAD}, a benchmark for unsupervised foreign-object anomaly detection with pixel-level localization in coal-stream scenes. We further propose a complementary-cue collaborative perception framework that extracts and fuses complementary anomaly evidence from three perspectives: object-level semantic composition modeling, semantic-attribution-based global deviation analysis, and fine-grained texture matching. The fused outputs provide robust image-level anomaly scoring and accurate pixel-level localization. Experiments on CoalAD demonstrate that our method outperforms widely used baselines across the evaluated image-level and pixel-level metrics, and ablation studies validate the contribution of each component. The code is available at https://github.com/xjpp2016/USAD.

翻译：在传送带煤流场景中实现可靠的外来物异常检测与像素级定位，对于安全智能的采矿作业至关重要。该任务因高度非结构化的环境而尤为困难：煤与矸石随机堆积、背景复杂多变、外来物常呈现低对比度、形变及遮挡，导致其与周围环境耦合。这些特性削弱了许多异常检测方法在结构化工业场景中所依赖的稳定性与规律性假设，造成显著的性能下降。为支持该场景下的评估与比较，我们构建了 \textbf{CoalAD}——一个面向煤流场景中无监督外来物异常检测与像素级定位的基准数据集。我们进一步提出一种互补线索协同感知框架，从三个视角提取并融合互补的异常证据：物体级语义构成建模、基于语义归属的全局偏差分析以及细粒度纹理匹配。融合后的输出可提供鲁棒的图像级异常评分与精确的像素级定位。在 CoalAD 上的实验表明，我们的方法在评估的所有图像级与像素级指标上均优于广泛使用的基线方法，消融研究验证了各组件的贡献。代码发布于 https://github.com/xjpp2016/USAD。