随着全球智慧城市建设的兴起,目标检测技术对优化城市功能、提高生活质量起着至关重要的作用。然而,现有的目标检测技术在精度方面仍然存在不足,实时性能,和适应性。为了应对这一挑战,本研究提出了一种创新的目标检测模型。我们的模型采用YOLOv8-DSAF的结构,包括三个关键模块:深度可分离卷积(DSConv),双路注意门模块(DPAG),和特征增强模块(FEM)。首先,DSConv技术优化了计算复杂度,在有限的硬件资源内实现实时目标检测。其次,DPAG模块引入了双通道注意力机制,允许模型有选择地专注于关键领域,从而提高高动态交通场景下的检测精度。最后,FEM模块突出了关键功能,以防止其损失,进一步提高检测精度。此外,我们提出了一个由四个主要层组成的物联网智慧城市框架:应用领域,物联网基础设施层,边缘层,和云层。该算法利用物联网基础设施层,边缘层,和云层实时收集和处理数据,实现更快的响应时间。在KITTIV和Cityscapes数据集上的实验结果表明,我们的模型优于YOLOv8模型。这表明,在复杂的城市交通场景中,我们的模型表现出优越的性能,具有更高的检测精度和适应性。我们相信,这一创新模式将极大地推动智慧城市的发展,推进目标检测技术。
With the rise of global smart city construction, target detection technology plays a crucial role in optimizing urban functions and improving the quality of life. However, existing target detection technologies still have shortcomings in terms of accuracy, real-time performance, and adaptability. To address this challenge, this study proposes an innovative target detection model. Our model adopts the structure of YOLOv8-DSAF, comprising three key modules: depthwise separable convolution (DSConv), dual-path attention gate module (DPAG), and feature enhancement module (
FEM). Firstly, DSConv technology optimizes computational complexity, enabling real-time target detection within limited hardware resources. Secondly, the DPAG module introduces a dual-channel attention mechanism, allowing the model to selectively focus on crucial areas, thereby improving detection accuracy in high-dynamic traffic scenarios. Finally, the
FEM module highlights crucial features to prevent their loss, further enhancing detection accuracy. Additionally, we propose an Internet of Things smart city framework consisting of four main layers: the application domain, the Internet of Things infrastructure layer, the edge layer, and the cloud layer. The proposed algorithm utilizes the Internet of Things infrastructure layer, edge layer, and cloud layer to collect and process data in real-time, achieving faster response times. Experimental results on the KITTI V and Cityscapes datasets indicate that our model outperforms the YOLOv8 model. This suggests that in complex urban traffic scenarios, our model exhibits superior performance with higher detection accuracy and adaptability. We believe that this innovative model will significantly propel the development of smart cities and advance target detection technology.