RegionNet: A Multi-Object Counting Method Based on Intelligent Region Partitioning

The counting of dense targets plays a crucial role in public safety, intelligent traffic management, and other fields, providing critical data support for multiple industries. However, existing counting methods are mostly suitable for visually specific categories and scenes with uniformly distributed targets. When facing complex scenarios with large fluctuations in target density and high freedom of movement, in reality, the effectiveness of these methods is greatly compromised. To address this issue, this study proposes RegionNet: a multi-object counting model based on intelligent region partitioning. Compared to existing methods, we have drawn inspiration from deep reinforcement learning principles to design a region selection module. This module optimizes the ranges of dense and sparse areas in images effectively and reasonably. Additionally, by introducing a dense region counting module and a sparse region counting module, which utilize density estimation and object detection techniques respectively, the model effectively addresses the counting tasks in different regions. This approach successfully overcomes the issue of reduced accuracy faced by traditional counting methods when dealing with significant variations in target distribution scales, thereby significantly enhancing the counting accuracy across various regions. Experimental results demonstrate that the method proposed in this paper significantly outperforms existing technologies in terms of counting performance on multi-visual category target datasets. It exhibits higher accuracy and exceptional robustness.