Abstract
Overlapping object perception aims to decouple the randomly overlapping foreground-background features, extracting foreground features while suppressing background features, which holds significant application value in fields such as security screening and medical auxiliary diagnosis. Despite some research efforts to tackle the challenge of overlapping object perception, most solutions are confined to the spatial domain. Through frequency domain analysis, we observe that the degradation of contours and textures due to the overlapping phenomenon can be intuitively reflected in the magnitude spectrum. Based on this observation, we propose a general Frequency-Optimized Anti-Overlapping Framework (FOAM) to assist the model in extracting more texture and contour information, thereby enhancing the ability for anti-overlapping object perception. Specifically, we design the Frequency Spatial Transformer Block (FSTB), which can simultaneously extract features from both the frequency and spatial domains, helping the network capture more texture features from the foreground. In addition, we introduce the Hierarchical De-Corrupting (HDC) mechanism, which aligns adjacent features in the separately constructed base branch and corruption branch using a specially designed consistent loss during the training phase. This mechanism suppresses the response to irrelevant background features of FSTBs, thereby improving the perception of foreground contour. We conduct extensive experiments to validate the effectiveness and generalization of the proposed FOAM, which further improves the accuracy of state-of-the-art models on four datasets, specifically for the three overlapping object perception tasks: Prohibited Item Detection, Prohibited Item Segmentation, and Pneumonia Detection. The code will be open source once the paper is accepted.
Abstract (translated)
重叠物体感知旨在解耦随机重叠的前景和背景特征，在提取前景特征的同时抑制背景特征，这一技术在安全检查和医学辅助诊断等领域具有重要的应用价值。尽管已有一些研究努力来应对重叠物体感知挑战，但大多数解决方案仍然局限于空间领域内。通过频域分析，我们观察到由于重叠现象导致边缘和纹理质量下降的情况可以直观地反映在幅度谱上。基于这一观察结果，我们提出了一种通用的频率优化抗重叠框架（FOAM），旨在帮助模型提取更多的纹理和轮廓信息，从而增强其对抗重叠物体感知的能力。 具体而言，我们设计了频域空间变换器模块（FSTB），该模块可以同时从频域和空间域中提取特征，有助于网络捕捉前景中的更多纹理特征。此外，我们引入了分层去混淆机制（HDC），在训练阶段通过特别设计的一致性损失将独立构建的基础分支和干扰分支的相邻特征对齐。这一机制抑制了FSTB对无关背景特征的响应，从而改善了前景轮廓的感知能力。 为了验证所提出的FOAM的有效性和泛化性能，我们进行了广泛的实验，并进一步提高了四个数据集上现有最先进模型在三种重叠物体感知任务——禁止物品检测、禁止物品分割和肺炎检测中的准确性。论文一旦被接受，代码将开源共享。
URL
https://arxiv.org/abs/2506.13501
