NDAM-YOLOseg: a real-time instance segmentation model based on multi-head attention mechanism

The primary objective of deep learning-based instance segmentation is to achieve accurate segmentation of individual objects in input images or videos. However, there exist challenges such as feature loss resulting from down-sampling operations, as well as complications arising from occlusion, deformation, and complex backgrounds, which impede the precise delineation of object instance boundaries. To address these challenges, we introduce a novel visual attention network called the Normalized Deep Attention Mechanism (NDAM) into the YOLOv8seg instance segmentation model, proposing a real-time instance segmentation method named NDAM-YOLOseg. Specifically, we optimize the feature processing methodology of YOLOv8-seg to mitigate the degradation in accuracy caused by information loss. Additionally, we introduce the NDAM to enhance the model’s discriminate focus on pivotal information, thereby further improving the accuracy of segmentation. Furthermore, a Boundary Refinement Module (BRM) is intended to enhance the segmentation of instance boundaries, resulting in an enhanced quality of mask generation. Our proposed method demonstrates competitive performance on multiple evaluation metrics across two widely-used benchmark datasets, namely MS COCO 2017 and KINS. In comparison to the baseline model YOLOv8x-seg, NDAM-YOLOseg achieves noteworthy improvements of 2.4 % and 2.5 % in terms of Average Precision (AP) on the aforementioned datasets, respectively.