Improved ResNet-34 Based Segmentation of Convergence and Shadow Zones in Transmission Loss Maps
摘要
To solve the segmentation of convergence zones and acoustic shadow zones in marine acoustic Transmission Loss (TL) maps, this paper proposes an improved ResNet-34-based method. It introduces cross-stage feature fusion in the backbone, uses deformable convolution to fit slender bent region boundaries, and combines a global statistical feature module and joint loss function for optimization. Validated via the gray value difference and uniformity indices on a Bellhop simulation-based TL dataset, experimental results show it effectively enhances segmentation performance, offering an interpretable and reusable technical path for automated TL map analysis and related engineering applications.