Reliable Detection of Unsafe Scenarios in Industrial Lines Using Deep Contrastive Learning with Bayesian Modeling

Current functional safety mechanisms mainly control the access points and perimeters of manufacturing cells without guaranteeing the integrity of their internal components or the absence of unauthorized humans or objects. In this work, we present a novel deep learning (DL)-based safety system that enhances the safety circuit designed according to functional safety principles, detecting, with great reliability, the presence of persons within the cell and, with high precision, anomalous elements of any kind. Our approach follows a two-stage DL methodology that combines contrastive learning with Bayesian clustering. First, a supervised contrastive scheme learns the characteristics of safe scenarios and distinguishes them from unsafe ones caused by workers remaining inside the cell. Next, a Bayesian mixture models the latent space of safe scenarios, quantifying deviations and enabling the detection of previously unseen anomalous objects without any specific fine-tuning. To further improve robustness, we introduce an ensemble-based hybrid latent-space methodology that maximizes performance regardless of the underlying encoders’ characteristics. The experiments are conducted on a real dataset captured in a belt-picking cell in production. The proposed system achieves 100% accuracy in distinguishing safe scenarios from those with the presence of workers, even in partially occluded cases, and an average area-under-the-curve of 0.9984 across seven types of anomalous objects commonly found in manufacturing environments. Finally, for interpretability analysis, we design a patch-based feature-ablation framework that demonstrates the model’s reliability under uncertainty and the absence of learning biases. The proposed technique enables the deployment of an innovative high-performance safety system that, to our knowledge, does not exist in the industry.