Salient object detection evaluation

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Salient object detection evaluation

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Salient object detection (SOD) aims to identify the most visually striking objects in an image, mimicking human visual attention. Evaluating SOD models rigorously requires well-designed metrics and standardized benchmarks to ensure fair comparisons.

Common evaluation metrics include precision-recall curves, F-measure, mean absolute error (MAE), and intersection-over-union (IoU). Precision measures the accuracy of detected salient regions, while recall assesses coverage of ground-truth objects. The F-measure combines both for balanced scoring. MAE calculates pixel-wise discrepancies between predictions and ground truth, sensitive to false positives/negatives. IoU evaluates spatial overlap accuracy.

Benchmark datasets like DUTS, ECSSD, and HKU-IS provide diverse images with pixel-level annotations. Challenges involve handling complex scenes, multi-object scenarios, and edge refinement. Trends now emphasize efficiency-speed tradeoffs and generalization across domains. Proper evaluation must account for both accuracy and computational costs to guide real-world applications.