Printed circuit boards (PCBs) are essential components of electronic devices, and ensuring their quality is crucial in their production. However, the vast variety of components and PCBs manufactured by different companies makes it challenging to adapt to production lines with speed demands. To address this challenge, we present a multi-view object detection framework that offers a fast and precise solution. We introduce a novel multi-view dataset with semi-automatic ground-truth data, which results in significant labeling resource savings. Labeling PCB boards for object detection is a challenging task due to the high density of components and the small size of the objects, which makes it difficult to identify and label them accurately. By training an object detector model with multi-view data, we achieve improved performance over single-view images. To further enhance the accuracy, we develop a multi-view inference method that aggregates results from different viewpoints. Our experiments demonstrate a 15% improvement in mAP for detecting components that range in size from 0.5 to 27.0 mm.

翻译：印刷电路板（PCB）是电子设备的核心组件，确保其质量在生产过程中至关重要。然而，不同公司生产的PCB和元件种类繁多，使得适应具有速度要求的生产线面临挑战。为解决这一问题，我们提出了一种多视角目标检测框架，能够提供快速而精确的解决方案。我们引入了一个新颖的多视角数据集，包含半自动标注的真实数据，从而显著节省标注资源。由于PCB元件密度高且目标尺寸小，准确识别和标注目标对象极具难度。通过使用多视角数据训练目标检测模型，我们实现了优于单视角图像的检测性能。为进一步提升精度，我们开发了一种多视角推理方法，用于聚合不同视角的检测结果。实验表明，在检测尺寸范围为0.5至27.0毫米的元件时，平均精度均值（mAP）提升了15%。