There can be numerous electronic components on a given PCB, making the task of visual inspection to detect defects very time-consuming and prone to error, especially at scale. There has thus been significant interest in automatic PCB component detection, particularly leveraging deep learning. However, deep neural networks typically require high computational resources, possibly limiting their feasibility in real-world use cases in manufacturing, which often involve high-volume and high-throughput detection with constrained edge computing resource availability. As a result of an exploration of efficient deep neural network architectures for this use case, we introduce PCBDet, an attention condenser network design that provides state-of-the-art inference throughput while achieving superior PCB component detection performance compared to other state-of-the-art efficient architecture designs. Experimental results show that PCBDet can achieve up to 2$\times$ inference speed-up on an ARM Cortex A72 processor when compared to an EfficientNet-based design while achieving $\sim$2-4\% higher mAP on the FICS-PCB benchmark dataset.
翻译:在一个特定多氯联苯上可能有许多电子部件,使视觉检查任务发现缺陷非常费时,容易出错,特别是在规模上。因此,对自动检测多氯联苯部件的兴趣很大,特别是利用深层的学习。然而,深神经网络通常需要高计算资源,可能限制其在制造中实际使用案例的可行性,这往往涉及高容量和高通量检测,并具有有限的边际计算资源可用性。由于为这一使用案例探索高效的深神经网络结构,我们引入了PCBDet,这是一个关注的浓缩网络设计,提供最先进的推论,同时实现高水平的多氯联苯部件检测性能,而与其他最先进的高效建筑设计相比。实验结果表明,PCBDDet可以在ARM Cortex A72处理器上实现高达2美元的时间加速,而与高效的网基设计相比,在FICS-PCB基准数据集上则达到2-4 ⁇ +++++++++++++++++++++++++++++++++/3+/3+/3+/3+/10+/10+/3+/3+/3+/1++/1+/3+++/3+/3+3+3+++++++++/2+/3++++3++++3+++++3+3++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++