Although the accurate lithological segmentation of outcrops plays a key role in hydrocarbon exploration, complex field environments and substantial scale variations within outcrops, particularly in extremely thin sand–mudstone interbeds, present considerable obstacles to precise segmentation. To overcome these complexities, we propose a Residual Attention Mechanism-Guided Asymptotic Feature Pyramid Network (AFPN-ResUNet). This architecture employs a structurally optimized RE-CBAM, which seamlessly integrates a Convolutional Block Attention Module (CBAM) into the residual network framework. This mechanism dynamically recalibrates channel and spatial feature responses, thereby effectively suppressing background artifacts while accentuating salient geological boundaries. Furthermore, we abandon traditional naive feature concatenation and instead utilize automatically generated spatially adaptive weights to guide the asymptotic fusion of features across different layers. This asymptotic fusion strategy effectively resolves the semantic discrepancies between distinct network levels, preserving the fine-grained spatial details crucial for delineating ultra-thin interbedded lithologies. To evaluate the architecture, a dedicated outcrop dataset was constructed. Compared to representative baselines (UNet, Vision Transformer, DeepLabV3+, PSPNet, and SegNeXt), AFPN-ResUNet achieves an mIoU of 93.41%, outperforming the baseline models by margins of 23.20%, 23.92%, 12.40%, 12.38%, and 26.04%, respectively. Additionally, ablation studies indicate that incorporating RE-CBAM and AFPN modules improves the mIoU by 13.11% and 13.98% over the backbone, respectively. These quantitative results demonstrate that AFPN-ResUNet effectively mitigates boundary blurring and preserves spatial continuity, an advantage visually corroborated by the Grad-CAM heatmaps. Notably, despite a relatively longer inference latency (33.99 ms), the model maintains a low computational overhead (179.79 G FLOPs), underscoring its practical application potential for outcrop lithology segmentation.
Tang et al. (Thu,) studied this question.