Computer Vision Based Conveyor Belt Congestion Recognition in Logistics Industrial Parks

Various automatic and intelligent technologies have been employed to facilitate the logistics operations in recent years promoted by the trend of industry 4.0. As the most frequently used automatic equipment in the logistics industrial parks, conveyor belt plays a critical role on the efficient sorting of packages. Due to the reasons like non-ideality of scheduling and inappropriate operations, conveyor belts can potentially be impacted by congestion, thereby inducing a series of consequences such as delay, lose and damage of packages. In order to tackle these issues, a computer vision-based method is proposed to recognize the congestion on conveyor belts. Other than the popular deep learning-based techniques, the proposed method comprehensively analyzes the characteristics of conveyor belt congestion using statistical approaches and extract informative features for decision making. Finally, the proposed method is evaluated on the data collected from real package sorting scenarios, where it outperforms the deep learning and conventional pattern recognition-based methods on both detection accuracy and capability of generalization.