Improved YOLOv8n for Lightweight Rail Surface Defect Detection

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Rail-surface defects are often small, low-contrast, and confused with steel texture or reflections, making it difficult to improve accuracy without increasing model cost. This study proposes ESiV-YOLOv8, a compact detector that assigns complementary modifications to feature selection, box regression, and multiscale fusion in YOLOv8n. EffectiveSE recalibrates deep backbone features, SIoU provides direction-aware regression, and a VoV-GSCSP/GSConv neck reduces redundant computation. Evaluation used a self-built four-class dataset of 4020 images, a near-duplicate-aware train-validation-test split, and matched seven-seed experiments. ESiV-YOLOv8 achieved 97.7% precision, 94.6% recall, 97.2% mAP@0.5, and 68.6% mAP@0.5:0.95. Relative to YOLOv8n, mAP@0.5:0.95 increased by 5.0 percentage points, while parameters and GFLOPs decreased by 17.1% and 12.2%, respectively. On the combined natural-condition subset, ESiV-YOLOv8 achieved 59.6% mAP@0.5:0.95, 5.6 percentage points above the baseline. The annotation audit yielded 98.0% class agreement and a mean box IoU of 0.89. Model-only and end-to-end latency increased by 1.4% and 2.2%, respectively. Overall, ESiV-YOLOv8 improves detection accuracy and reduces model scale with limited latency overhead on the tested backend.