公共安全是一个至关重要的问题,通常通过公共场所入口的安全检查来解决,让受过训练的人员或X射线扫描仪检测违禁物品。然而,很多地方,比如医院,学校,活动中心缺乏这样的资源,冒着安全漏洞的风险。即使使用X射线扫描仪或手动检查,漏洞可以被恶意意图的个人利用,带来重大安全隐患。此外,传统方法,依靠人工检查和常规图像处理技术,通常效率低下,容易出现高错误率。为了减轻这些风险,我们提出了一个实时检测模型-EnhanceNet,它使用集成到YOLOv4中的自定义扩展增强池网络(SEP-Net)。创新的SEP-Net增强了特征表示和定位精度,显著有助于模型在检测违禁物品方面的功效。我们注释了九个类的自定义数据集,并使用不同的输入大小(608和416)评估了我们的模型。608输入大小实现了74.10%的平均平均精度(mAP),检测速度为每秒22.3帧(FPS)。416输入大小显示出优越的性能,MAP为76.75%,检测速度为27.1FPS。这些证明了我们的模型是准确和有效的,使它们适合实时应用。
Public safety is a critical concern, typically addressed through security checks at entrances of public places, involving trained officers or X-ray scanning machines to detect prohibited items. However, many places like hospitals, schools, and event centres lack such resources, risking security breaches. Even with X-ray scanners or manual checks, gaps can be exploited by individuals with malicious intent, posing significant security risks. Additionally, traditional methods, relying on manual inspections and conventional image processing techniques, are often inefficient and prone to high error rates. To mitigate these risks, we propose a real-time detection model - EnhanceNet using a customized Scale-Enhanced Pooling Network (SEP-Net) integrated into the YOLOv4. The innovative SEP-Net enhances feature representation and localization accuracy, significantly contributing to the model\'s efficacy in detecting prohibited items. We annotated a custom dataset of nine classes and evaluated our models using different input sizes (608 and 416). The 608 input size achieved a mean Average Precision (mAP) of 74.10% with a detection speed of 22.3 Frames per Second (FPS). The 416 input size showed superior performance, achieving a mAP of 76.75% and a detection speed of 27.1 FPS. These demonstrate that our models are accurate and efficient, making them suitable for real-time applications.