Prohibited item detection is crucial for the safety of public places. Deep learning, one of the mainstream methods in prohibited item detection tasks, has shown superior performance far beyond traditional prohibited item detection methods. However, most neural network architectures in deep learning still lack sufficient local feature representation ability for overlapping and small targets, and ignore the problem of semantic conflicts caused by direct feature fusion. In this paper, we propose MFPIDet, a novel prohibited item detection neural network architecture based on improved YOLOV7 to achieve reliable prohibited item detection in complex environments. Specifically, a multi-scale attention module (MAM) backbone is proposed to filter the redundant information of target regions and further applied to enhance the local feature representation ability of overlapping objects. Here, to reduce the redundant information of target regions, a squeeze-excitation (SE) block is used to filter the background. Then, aiming at enhancing the feature expression ability of overlapping objects, a multi-scale feature extraction module (MFEM) is designed for local feature representation. In addition, to obtain richer context information, We design an adaptive fusion feature pyramid network (AF-FPN) to combine the adaptive context information fusion module (ACIFM) with the feature fusion module (FFM) to improve the neck structure of YOLOV7. The proposed method is validated on the PIDray dataset, and the tested results showed that our method obtained the highest mAP (68.7%), which is improved by 3.5% than YOLOV7 methods. Our approach provides a new design pattern for prohibited item detection in complex environments and shows the development potential of deep learning in related fields.

违禁物品检测对于公共场所的安全至关重要。深度学习作为违禁物品检测任务的主流方法之一，表现出了远远超越传统违禁物品检测方法的优越性能。然而，深度学习中的大多数神经网络架构对于重叠和小目标仍然缺乏足够的局部特征表示能力，并且忽略了直接特征融合引起的语义冲突问题。在本文中，我们提出了MFPIDet，一种基于改进的YOLOV7的新型违禁物品检测神经网络架构，以在复杂环境中实现可靠的违禁物品检测。具体来说，提出了多尺度注意模块（MAM）主干来过滤目标区域的冗余信息，并进一步应用于增强重叠对象的局部特征表示能力。这里，为了减少目标区域的冗余信息，使用挤压激励（SE）块来过滤背景。然后，为了增强重叠对象的特征表达能力，设计了多尺度特征提取模块（MFEM）用于局部特征表示。此外，为了获得更丰富的上下文信息，我们设计了自适应融合特征金字塔网络（AF-FPN），将自适应上下文信息融合模块（ACIFM）与特征融合模块（FFM）相结合，以改进YOLOV7的颈部结构。所提出的方法在PIDray数据集上进行了验证，测试结果表明我们的方法获得了最高的mAP （68.7％），比YOLOV7方法提高了3.5％。 我们的方法为复杂环境中的违禁物品检测提供了一种新的设计模式，并展示了深度学习在相关领域的发展潜力。