To address the accuracy degradation in detecting occluded and complex stacked parts, an improved YOLO11-based object detection algorithm named YOLO11-MBM is proposed. The method integrates a multidimensional collaborative attention (MCA) mechanism and employs weighted feature fusion. The main strategies include three aspects: Firstly, an MCA module is incorporated into the C3k2 module of the backbone network to fuse channel, spatial and scale attention, which enhances the ability to distinguish texture-similar parts and effectively suppresses false positives caused by texture confusion. Secondly, a weighted feature fusion architecture BiFPN-SFF-Concat is designed in the feature fusion network. Through bidirectional feature propagation and a scale-sensitive dynamic feature weighting strategy, it improves feature complementarity and detail representation in occluded scenarios, reducing missed detections. Finally, the minimum point distance intersection over union (MPDIoU) is adopted as the loss function to improve localization accuracy and convergence speed. Experimental results on the extended Baidu Paddle industrial parts dataset show that YOLO11-MBM achieves an mAP50 of 87.8% and an mAP50:95 of 58.0%, outperforming the baseline model by 6% and 9% respectively, while maintaining realtime performance (110 fps on V100 GPU) and lightweight design (with only a 1.1×106 parameter increase). The experimental results confirm its practicality in complex industrial inspection scenarios and furnish a feasible technical solution for visual inspection systems in smart manufacturing.