An improved YOLOv8n-based target position detection algorithm is proposed to address the in-efficiency of target detection in non-cooperative robotic arm gripping tasks.First,the Large Separable Ker-nel Attention(LSKA)mechanism is integrated into the Spatial Pyramid Pooling Fusion(SPPF)layer to enhance the model's multiscale feature aggregation capability.Second,a novel lightweight module,RGC-SPELAN,is designed to reduce the computational cost and runtime of the model.Additionally,the aver-age pairwise distance intersection over union(MPDIoU)is restructured with an inner transformation con-cept,which is further combined with the weighted intersection over union(Wise-IoU)to develop a new loss function,Wise-MPDIoU^inner.This loss function enhances both the training efficiency and detection performance of the model.Finally,using target position detection and depth information,a real-time coor-dinate system is constructed to determine the target's 3D spatial attitude,enabling the completion of robot-ic arm grasping tasks.Experimental results demonstrate that the proposed algorithm achieves an accuracy of 96.5%,an mAP@0.5 of 96.7%,a 16%reduction in parameters,and a 33%improvement in infer-ence speed.The algorithm effectively balances model accuracy and computational efficiency,meeting the real-time requirements of the UR5 robot for non-cooperative grasping tasks.
维普
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