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：本文深入探讨了YOLO架构在监测菲舍尔技术工业4.0应用流程中的适用性。具体而言，我们采用了不同尺寸与复杂度设计的YOLO架构，并结合了多种先验形状分配策略。为模拟真实工厂环境，我们准备了丰富的增强数据集，通过引入多种失真来显著提升或某些情况下降低图像质量。其中，降质处理用于模拟环境变化，而增强操作则旨在补偿数据集制备过程中面临的色彩相关性。实验分析表明，所提出的方法在不同评估指标下均有效，同时我们针对问题固有且不可避免的色彩相关性，定制了相应的训练与验证策略。