印刷电路板(PCB)作为现代电子设备的核心组成部分,随着技术的进步,在多个领域得到了广泛的应用,包括消费电子、通信、医疗设备、汽车、工业控制等。针对PCB传统缺陷检测方法过程中的成本高、效率低、漏检率高,并且常见PCB缺陷微小,检测...印刷电路板(PCB)作为现代电子设备的核心组成部分,随着技术的进步,在多个领域得到了广泛的应用,包括消费电子、通信、医疗设备、汽车、工业控制等。针对PCB传统缺陷检测方法过程中的成本高、效率低、漏检率高,并且常见PCB缺陷微小,检测精度低等问题,提出一种基于改进YOLOv8的深度学习检测方法。1) 引进ASF-YOLO网络,并且在此基础上添加小目标检测层;2) 在主干网络中加入SimAM注意力机制;3) 改进损失函数为Wise_CIoUv3。实验表明,改进后的模型的平均精度mAP达到90.4%,相比基线模型提高3.55%。另外,模型参数量下降17.19%,模型大小减少0.8 MB,实现了模型部分轻量化。为此领域提供了参考和应用价值。As a core component of modern electronic equipment, printed circuit board (PCB) has been widely used in many fields with the progress of technology, including consumer electronics, communications, medical equipment, automobiles, industrial control and so on. Aiming at the problems of high cost, low efficiency, high missed detection rate, small PCB defects and low detection accuracy in traditional PCB defect detection methods, a deep learning detection method based on improved YOLOv8 was proposed. 1) Introduce ASF-YOLO network, and add small target detection layer on this basis;2) Add the SimAM attention mechanism to the backbone network;3) Improve the loss function to Wise_CIoUv3. Experiments show that the average precision mAP of the improved model reaches 90.4%, which is 3.55% higher than that of the baseline model. In addition, the number of model parameters decreased by 17.19%, the size of the model decreased by 0.8 MB, and the initial lightweight of the model was realized. It provides reference and application value in this field.展开更多
文摘印刷电路板(PCB)作为现代电子设备的核心组成部分,随着技术的进步,在多个领域得到了广泛的应用,包括消费电子、通信、医疗设备、汽车、工业控制等。针对PCB传统缺陷检测方法过程中的成本高、效率低、漏检率高,并且常见PCB缺陷微小,检测精度低等问题,提出一种基于改进YOLOv8的深度学习检测方法。1) 引进ASF-YOLO网络,并且在此基础上添加小目标检测层;2) 在主干网络中加入SimAM注意力机制;3) 改进损失函数为Wise_CIoUv3。实验表明,改进后的模型的平均精度mAP达到90.4%,相比基线模型提高3.55%。另外,模型参数量下降17.19%,模型大小减少0.8 MB,实现了模型部分轻量化。为此领域提供了参考和应用价值。As a core component of modern electronic equipment, printed circuit board (PCB) has been widely used in many fields with the progress of technology, including consumer electronics, communications, medical equipment, automobiles, industrial control and so on. Aiming at the problems of high cost, low efficiency, high missed detection rate, small PCB defects and low detection accuracy in traditional PCB defect detection methods, a deep learning detection method based on improved YOLOv8 was proposed. 1) Introduce ASF-YOLO network, and add small target detection layer on this basis;2) Add the SimAM attention mechanism to the backbone network;3) Improve the loss function to Wise_CIoUv3. Experiments show that the average precision mAP of the improved model reaches 90.4%, which is 3.55% higher than that of the baseline model. In addition, the number of model parameters decreased by 17.19%, the size of the model decreased by 0.8 MB, and the initial lightweight of the model was realized. It provides reference and application value in this field.
