Terahertz imaging technology has been widely used in security inspections due to its ability to detect various concealed hazardous materials and the advantage of being harmless to the human body. However, limited by the terahertz imaging system, it is challenging to detect concealed objects due to hard samples and imbalanced categories caused by terahertz image quality. To solve these issues, we propose a hybrid network with difficult–easy learning (DEL) for concealed object detection in the imbalanced activated terahertz image dataset. Based on the one-stage framework YOLOv5m, a path aggregation hybrid structure (PAHS) is proposed to improve the performance of the proposed network while maintaining real-time detection. Specifically, PAHS with transformer block (TB) and a fine-tuned global context attention (GCA) are designed to fully exploit and fuse the multi-scale information by path aggregation, which improves the detection accuracy of low contrast and noise-interfered objects. To solve the problem of imbalanced categories in the activated terahertz dataset, a DELoss is developed to guide the network classification. Moreover, EIOU is adopted to boost the network training, and a modified B-Ocl loss is used to discriminate the positive and negative samples. Experiments are conducted on a public imbalanced activate terahertz image dataset. The experimental results illustrate that the proposed network achieves competitive performance compared with recently reported state-of-the-art detection methods. Moreover, the proposed method improves the balanced detection ability of different categories.

太赫兹成像技术因其能够检测各种隐藏的危险物质和对人体无害的优势，在安检中得到了广泛的应用。然而，受太赫兹成像系统的限制，由于太赫兹图像质量导致的硬样本和类别不平衡，检测隐藏的物体具有挑战性。为了解决这些问题，我们提出了一个具有难易学习 （DEL） 的混合网络，用于在不平衡的激活太赫兹图像数据集中进行隐藏对象检测。基于一阶段框架 YOLOv5m，提出了一种路径聚合混合结构 （PAHS），以提高所提网络的性能，同时保持实时检测。具体来说，具有变压器块 （TB） 和微调全局上下文注意力 （GCA） 的 PAHS 旨在通过路径聚合来充分利用和融合多尺度信息，从而提高了低对比度和噪声干扰目标的检测精度。为了解决激活太赫兹数据集中类别不平衡的问题，开发了 DELoss 来指导网络分类。此外，采用 EIOU 来促进网络训练，并使用改进的 B-Ocl 损失来区分正样本和负样本。在公共不平衡激活太赫兹图像数据集上进行实验。实验结果表明，与最近报道的最先进的检测方法相比，所提出的网络实现了有竞争力的性能。此外，所提方法提高了不同类别的平衡检测能力。