Instance Segmentation of Cluttered Mechanical Parts Based on Improved YOLACT
In order to address the issue of poor instance segmentation accuracy for parts instances in a clut-tered environment,an improved YOLACT algorithm is proposed.Multi-level feature fusion and channel at-tention mechanism modules ( MLCA) are introduced into the C3 and C4 layers of the backbone network,optimizing the precision of feature extraction.At the same time,to ensure the image acquires multi-scale field-of-view information,an advanced contextual feature pyramid module ( AC-FPN) structure replaces the conventional FPN pyramid to capture broader receptive fields,facilitating accurate prediction.The net-work was trained and tested using a custom-made dataset of stacked parts.Comparative experiments demon-strate that the improved YOLACT algorithm,without significantly increasing the run time,exhibits superior detection and segmentation performance compared to the original algorithm.
万方数据
维普
