Slender Flexible Object Segmentation Based on Object Correlation Module and Loss Function Optimization

Abstract

Slender flexible objects are ubiquitous in real-world circumstances. The existing object detection and segmentation algorithms have achieved high accuracy and speed in rigid objects, but the detection effect of slender flexible objects is not ideal. Extreme aspect ratios and dynamically changeable geometric appearance characterize slender flexible objects, to the extent that it is difficult to locate them in instance segmentation. In this paper, a new instance segmentation method based on the object correlation module and loss function optimization is proposed for the detection of slender flexible objects. In order to achieve more accurate anchor box positioning, a GIoU bounding-box regression loss function is selected to overcome the problem of inconsistency between training objectives and assessment indicators. Furthermore, due to the Mask Scoring R-CNN detection network ignoring the relationships between objects, the object correlation module is proposed to achieve end-to-end learning and modeling of the correlation features between all objects in the image to improve slender flexible objects detection accuracy. The results of the experiments on the self-built flexible object dataset for the power grid operation sites demonstrate that the method presented in this research can efficiently recognize and segment slender flexible objects, with a detection accuracy of 44.8%. The ablation experiment also shows that the addition object correlation module and the revised bounding-box regression loss function are both effective and can enhance slender flexible object detection accuracy by 1.2% and 0.5%, respectively. The proposed instance segmentation method considers the correlation characteristics between objects and improves the bounding-box regression loss function to increase the segmentation accuracy of slender flexible objects.