In this paper we extensively explore the suitability of YOLO architectures to monitor the process flow across a Fischertechnik industry 4.0 application. Specifically, different YOLO architectures in terms of size and complexity design along with different prior-shapes assignment strategies are adopted. To simulate the real world factory environment, we prepared a rich dataset augmented with different distortions that highly enhance and in some cases degrade our image qualities. The degradation is performed to account for environmental variations and enhancements opt to compensate the color correlations that we face while preparing our dataset. The analysis of our conducted experiments shows the effectiveness of the presented approach evaluated using different measures along with the training and validation strategies that we tailored to tackle the unavoidable color correlations that the problem at hand inherits by nature.

翻译：在本文中,我们广泛探讨YOLO结构是否适合监测Fischertechnik工业4.0应用过程流。具体地说,采用了不同的YOLO结构在规模和复杂性设计上的不同结构,以及不同的先前形状分配战略。为模拟真实的世界工厂环境,我们准备了一个丰富的数据集,该数据集增加了不同的扭曲,大大强化了我们的图像质量,在某些情况下还降低了我们的图像质量。进行降解是为了考虑到环境的变异和增强,以弥补我们在准备数据集时所面临的颜色相关性。我们进行的实验分析表明,采用不同措施评估的提出方法的有效性,以及我们为处理自然继承问题不可避免的颜色相关性而设计的培训和验证战略。