Optical vision-based dynamic target recognition model for industrial robots using improved YOLOv13

Abstract

Real-time and precise recognition of moving targets by industrial robots based on optical vision in dynamic production environments is key to realizing intelligent grasping and assembly. Existing detection methods still suffer from insufficient feature representation capability and difficulty in balancing detection speed and accuracy when dealing with rapid changes in target scale, motion blur, optical reflection interference and complex background noise. To address these issues, this paper proposes an improved YOLOv13 (DCA-YOLOv13) recognition model. The model introduces a deformable convolution module into the backbone network to enhance the geometric adaptation capability for non-rigidly deformed targets, and designs a lightweight channel attention mechanism to suppress complex background noise. At the same time, a multi-scale feature gold tower is integrated to improve sensitivity to small targets. Experiments conducted on a self-built optical dynamic workpiece image dataset show that the improved model achieves an enhanced mean average precision (mAP) of 94.6% and an inference speed of 82 FPS, meeting the real-time requirements of industrial sites. This model provides an efficient and stable optical visual perception scheme for robotic dynamic grasping.