Benchmarking Dense and Indiscernible Object Counting with Blueberries

Real-world agricultural counting often operates in the extreme regime of Dense and Indiscernible Object Counting (DIOC), where targets are tiny, clustered, and highly camouflaged. To facilitate research in this domain, we introduce DIOCblueberry, a large-scale benchmark that pushes the boundaries of visual perception. Unlike general datasets with salient objects, DIOCblueberry features extreme occlusion and camouflage. Compared to the popular FSC147 benchmark, it contains 1.9$\times$ more instances per image (avg. 108) with an average box pixel ratio that is 7.9$\times$ smaller, serving as a rigorous testbed for model robustness. Standard counting methods struggle in these scenarios due to severe visual ambiguity and scale mismatch. To address this, we propose MaskCount, a coarse-to-fine framework that incorporates semantic guidance. MaskCount leverages Vision-Language Models (CLIP) to generate pseudo segmentation masks for background suppression and employs a contrastive loss to maximize feature discriminability between fruits and foliage. Additionally, we design an edge-aware cropping mechanism to resolve boundary truncation in dense clusters. Extensive experiments demonstrate that MaskCount achieves a new state-of-the-art, reducing MAE and RMSE by 49.16% and 70.50% respectively on DIOCblueberry, with strong generalization to other agricultural scenes.