Hybrid Convolutional Network for Object Detection and Multi-Class Counting

In this work, we introduce a novel Hybrid Convolutional Network designed for efficient object detection and multi-class counting in varied applications such as aerial photography and surveillance. Leveraging the strengths of hybrid networks, our model facilitates the simultaneous execution of detection and counting tasks by sharing common network structures, thereby accelerating the image analysis process and enhancing feature generalization. We propose a novel Density-Aware Non-Maximum Suppression algorithm that adaptively adjusts the Intersection over Union IoU threshold according to object density, ensuring robust detection performance in both dense and sparse scenes. Additionally, we introduce a Region Suppression Module that leverages detection outcomes to minimize noise in density maps, further improving counting accuracy. Through comprehensive experiments, our approach demonstrates state-of-the-art performance in counting tasks and competitive accuracy in detection tasks across various datasets, while maintaining high processing speed.