Burst point of projectile instance segmentation method based on improved SOLOv2
Xinan Lian, Chen Dong, Xiaowei Sherry Yan · 2024
In the field of image processing, particularly in the realm of analyzing bomb blast sites, the speed and accuracy of fine-grained segmentation have been longstanding concerns. To address these challenges, this study proposes an innovative approach based on an enhanced version of the SOLOv2 framework. By integrating low-level feature information extracted from the backbone network into the prediction network, and replacing top-level feature extraction with sub-top-level information, both the segmentation speed and accuracy of bomb blast sites are significantly improved. The experimental results demonstrate that the enhanced SOLOv2 achieves a segmentation speed of 20.35 frames per second (FPS) for small-scale blast sites, with a 3.21 improvement in precision. Notably, the average accuracy of blast site segmentation reaches an impressive 39.56. The segmentation test results robustly indicate that the enhanced SOLOv2 not only ensures efficient segmentation of small-scale bomb blast sites, but also enhances overall segmentation accuracy, thereby highlighting its superior segmentation performance.