OSLTSF-YOLO: Oriented Rail Surface Defect Detection via Orientation-Adaptive Slender-Strip Receptive Fields and Local Texture-Aware Scale Fusion

Rail surface defects frequently exhibit arbitrary orientations, elongated geometries, small spatial footprints, and substantial texture-scale variation. Although oriented bounding boxes (OBBs) improve geometric localization, they do not directly address insufficient directional-structure modeling in the backbone or the limited adaptability of fixed-scale feature fusion in the neck. This study proposes OSLTSF-YOLO, a lightweight oriented detector built on YOLO11n-OBB. An orientation-adaptive slender strip receptive field (OSRF) module is inserted at the backbone P4 level to encode elongated structures through horizontal, vertical, and two diagonal strip-convolution branches with pixel-wise directional routing. A local texture-aware scale fusion (LTSF) module is introduced at the neck P4 level to combine multi-dilation depthwise features using global and local texture routing followed by spatial response modulation. Both modules use small-magnitude non-zero bounded residual injection to preserve their input features at initialization. On an in-house dataset with an image-level random split, the complete model achieved a three-seed mean mAP50 of 88.47% and F1 of 87.91%, compared with 83.91% and 84.94%, while using 3.17 M parameters and 6.86 GFLOPs and achieving 118.0 FPS. These results support structured modeling of directional receptive fields and texture-dependent scales for lightweight oriented rail defect detection.