To address the selection of generic image segmentation and open-set object detection models for roboticobjectrecognition, this paper experimentally compares the generic image segmentation and open-set object detecti...
详细信息
ISBN:
(纸本)9798350352634;9798350352627
To address the selection of generic image segmentation and open-set object detection models for roboticobjectrecognition, this paper experimentally compares the generic image segmentation and open-set object detection models, specifically, compares and analyzes the performance of the SAM (Segment anything Model) image segmentation model as well as its derivatives Mobile SAM and HQ-SAM generic image segmentation model, and the Grounding DINO and YOLO-world open set object detection models. The applicability of these state-of-the-art objectrecognition methods in different robotic environment perception tasks is fully validated through detailed experimental evaluations. The results show that Mobile SAM and YOLO-world perform well in real-time demanding scenarios and can meet the needs of fast response and processing, while HQ-SAM and Grounding DINO show superior performance in segmentation or detection tasks requiring high accuracy and are more suitable for application scenarios requiring high segmentation or detection accuracy. These findings provide references and guidance for the practical applications of roboticobjectrecognition, and can help researchers and engineers to choose the most suitable models and methods in scenarios with different requirements.
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