🤖 AI Summary
This study systematically evaluates the performance and robustness of various YOLO models for object detection in robotic workspaces. By constructing a custom dataset tailored to robotic scenarios, integrating the COCO2017 benchmark, and incorporating image distortions to simulate real-world deployment conditions, the work presents the first comprehensive comparison of different YOLO variants in this specific context. The experimental results reveal significant differences among the models in terms of accuracy, inference speed, and resilience to visual perturbations. These findings provide empirical evidence and practical guidance for selecting appropriate YOLO architectures in robotic vision systems, balancing trade-offs between detection precision, computational efficiency, and robustness under realistic operating conditions.
📝 Abstract
YOLO object detectors recently became a key component of vision systems in many domains. The family of available YOLO models consists of multiple versions, each in various variants. The research reported in this paper aims to validate the applicability of members of this family to detect objects located within the robot workspace. In our experiments, we used our custom dataset and the COCO2017 dataset. To test the robustness of investigated detectors, the images of these datasets were subject to distortions. The results of our experiments, including variations of training/testing configurations and models, may support the choice of the appropriate YOLO version for robotic vision tasks.