Xiao Linsong, Li Wenzao, Yao Sai, Liu Hantao, Ren Dehao
School of Communication Engineering, Chengdu University of Information Technology, Chengdu, 610225, Sichuan, China.
Educational Informationization and Big Data Center, Education Department of Sichuan Province, Chengdu, 610015, Sichuan, China.
Sci Rep. 2024 Oct 9;14(1):23542. doi: 10.1038/s41598-024-75243-1.
The proliferation of edge devices driven by advancements in Internet of Things (IoT) technology has intensified the challenge of achieving high-precision small target detection, as it demands extensive computational resources. This amplifies the conflict between the need for precise detection and the requirement for cost-efficiency across numerous edge devices. To solve this problem, this paper introduces an enhanced target detection algorithm, MSGD-YOLO, built upon YOLOv8. The Faster Implementation of CSP Bottleneck with 2 convolutions (C2f) module is enhanced through the integration of the Ghost module and dynamic convolution, resulting in a more lightweight architecture while enhancing feature generation. Additionally, Spatial Pyramid Pooling with Enhanced Local Attention Network (SPPELAN) replaces Spatial Pyramid Pooling Fast (SPPF) to expand the receptive field, optimizing multi-level feature aggregation for improved performance. Furthermore, a novel Multi-Scale Ghost Convolution (MSGConv) and Multi-Scale Generalized Feature Pyramid Network (MSGPFN) are introduced to enhance feature fusion and integrate multi-scale information. Finally, four optimized dynamic convolutional detection heads are employed to capture target features more accurately and improve small target detection precision. Evaluation on the VisDrone2019 dataset shows that compared with YOLOv8-n, MSGD-YOLO improves mAP@50 and mAP@50-95 by 14.1% and 11.2%, respectively. In addition, the model not only achieves a 16.1% reduction in parameters but also attains a processing speed of 24.6 Frames Per Second (FPS) on embedded devices, thereby fulfilling real-time detection requirements.
物联网（IoT）技术的进步推动了边缘设备的激增，这加剧了实现高精度小目标检测的挑战，因为它需要大量的计算资源。这加剧了众多边缘设备在精确检测需求与成本效益要求之间的冲突。为了解决这个问题，本文介绍了一种基于YOLOv8构建的增强型目标检测算法MSGD-YOLO。通过集成Ghost模块和动态卷积，增强了具有2个卷积的CSP瓶颈更快实现（C2f）模块，从而在增强特征生成的同时实现了更轻量级的架构。此外，具有增强局部注意力网络的空间金字塔池化（SPPELAN）取代了空间金字塔池化快速（SPPF），以扩大感受野，优化多级特征聚合以提高性能。此外，还引入了一种新颖的多尺度Ghost卷积（MSGConv）和多尺度广义特征金字塔网络（MSGPFN）来增强特征融合并整合多尺度信息。最后，采用四个优化的动态卷积检测头来更准确地捕捉目标特征并提高小目标检测精度。在VisDrone2019数据集上的评估表明，与YOLOv8-n相比，MSGD-YOLO的mAP@50和mAP@50-95分别提高了14.1%和11.2%。此外，该模型不仅参数减少了16.1%，而且在嵌入式设备上达到了24.6帧每秒（FPS）的处理速度，从而满足了实时检测要求。
