Dr. Dong Chen Profile

Dr. Dong Chen

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Proceedings Article | 27 March 2024 Paper

Burst point of projectile instance segmentation method based on improved SOLOv2

Xinan Lian, Dong Chen, Xiaowei Yan

Proceedings Volume 13105, 131053M (2024) https://doi.org/10.1117/12.3026726

KEYWORDS: Image segmentation, Explosives, Mathematical optimization, Bombs, Feature extraction, Performance modeling, Object detection, Data modeling, Convolution, Semantics

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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.

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