This paper employs a model fusion strategy to accurately detect objects in extremely dark images. Multiple models are trained: one using dark images, one using enhanced images, and one using augmented data. During testing, image enhancements and transformations are applied to improve detection. Finally, a clustering method aggregates predictions, selecting optimal results.