Luo Shiyi, Wan Fang, Lei Guangbo, Xu Li, Ye Zhiwei, Liu Wei, Zhou Wen, Xu Chengzhi
School of Computer Science, Hubei University of Technology, Wuhan 430068, China.
Sensors (Basel). 2024 Jul 5;24(13):4363. doi: 10.3390/s24134363.
Electronic components are the main components of PCBs (printed circuit boards), so the detection and classification of ECs (electronic components) is an important aspect of recycling used PCBs. However, due to the variety and quantity of ECs, traditional target detection methods for EC classification still have problems such as slow detection speed and low performance, and the accuracy of the detection needs to be improved. To overcome these limitations, this study proposes an enhanced YOLO (you only look once) network (EC-YOLOv7) for detecting EC targets. The network uses ACmix (a mixed model that enjoys the benefits of both self-attention and convolution) as a substitute for the 3 × 3 convolutional modules in the E-ELAN (Extended ELAN) architecture and implements branch links and 1 × 1 convolutional arrays between the ACmix modules to improve the speed of feature retrieval and network inference. Furthermore, the ResNet-ACmix module is engineered to prevent the leakage of function data and to minimise calculation time. Subsequently, the SPPCSPS (spatial pyramid pooling connected spatial pyramid convolution) block has been improved by replacing the serial channels with concurrent channels, which improves the fusion speed of the image features. To effectively capture spatial information and improve detection accuracy, the DyHead (the dynamic head) is utilised to enhance the model's size, mission, and sense of space, which effectively captures spatial information and improves the detection accuracy. A new bounding-box loss regression method, the WIoU-Soft-NMS method, is finally suggested to facilitate prediction regression and improve the localisation accuracy. The experimental results demonstrate that the enhanced YOLOv7 net surpasses the initial YOLOv7 model and other common EC detection methods. The proposed EC-YOLOv7 network reaches a mean accuracy (mAP@0.5) of 94.4% on the PCB dataset and exhibits higher FPS compared to the original YOLOv7 model. In conclusion, it can significantly enhance high-density EC target recognition.
电子元件是印刷电路板（PCB）的主要组成部分，因此电子元件（EC）的检测与分类是废旧印刷电路板回收利用的一个重要方面。然而，由于电子元件的种类繁多、数量庞大，传统的用于电子元件分类的目标检测方法仍然存在检测速度慢、性能低等问题，检测精度有待提高。为克服这些局限性，本研究提出一种用于检测电子元件目标的增强型YOLO（你只看一次）网络（EC-YOLOv7）。该网络使用ACmix（一种兼具自注意力和卷积优点的混合模型）替代E-ELAN（扩展ELAN）架构中的3×3卷积模块，并在ACmix模块之间实现分支连接和1×1卷积阵列，以提高特征检索速度和网络推理速度。此外，设计了ResNet-ACmix模块以防止功能数据泄漏并减少计算时间。随后，通过将串行通道替换为并行通道对SPPCSPS（空间金字塔池化连接空间金字塔卷积）模块进行了改进，提高了图像特征的融合速度。为有效捕捉空间信息并提高检测精度，利用DyHead（动态头部）增强模型的尺寸、任务和空间感，有效捕捉空间信息并提高检测精度。最后提出一种新的边界框损失回归方法——WIoU-Soft-NMS方法，以促进预测回归并提高定位精度。实验结果表明，增强型YOLOv7网络优于初始YOLOv7模型和其他常见的电子元件检测方法。所提出的EC-YOLOv7网络在PCB数据集上的平均精度（mAP@0.5）达到94.4%，与原始YOLOv7模型相比具有更高的每秒帧数（FPS）。总之，它可以显著提高高密度电子元件目标识别能力。
