Automated control of robots in the work zone: a YOLO-Based approach for optimized maintenance

Résumé

Ensuring the safe and reliable operation of industrial robots requires effective monitoring systems capable of detecting abnormal situations within robotic workspaces. In the context of industry 4.0, computer vision techniques offer promising solutions for real-time supervision of industrial environments. This study proposes a vision-based monitoring framework designed to detect the position of an industrial robot relative to predefined operating and restricted zones. A custom dataset was created from a camera installed above the robot workspace, including images captured under different operational conditions. The models were trained to classify robot positions and detect their status in order to alert maintenance personnel when robots leave predefined zones and optimize interventions. The proposed system relies on deep learning object detection and instance segmentation models based on recent YOLO architectures. A comparative evaluation between YOLOv8 and YOLOv11 was first conducted to identify the most suitable architecture for the considered monitoring task. Experimental results show that both models achieve high detection accuracy, with mAP50 values exceeding 98%; while YOLOv8 demonstrates slightly better localization performance and training stability. Consequently, YOLOv8 was selected for further experiments. To evaluate the robustness and generalization capability of the proposed system, a cross-validation protocol was designed using multiple camera viewpoints and varying illumination conditions. The results indicate that multi-view training significantly improves detection robustness, while strict cross view experiments reveal the strong influence of viewpoint variation on model performance. In contrast, illumination changes have a comparatively smaller impact detection accuracy. Overall, the proposed monitoring framework highlights the potential of the models to significantly reduce unplanned downtime and boost the vigilance of maintenance operators, demonstrating their relevance in the context of Industry 4.0.

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