Hybrid Pooling and Convolutional Network for Improving Accuracy and Training Convergence Speed in Object Detection

TL;DR

This paper presents HPC-Net, a novel object detection network that combines hybrid pooling and convolutional techniques to enhance accuracy and accelerate training convergence.

Contribution

The paper introduces HPC-Net, integrating hybrid pooling with convolutional layers to improve detection accuracy and training speed.

Findings

Achieves higher detection accuracy compared to baseline models.

Converges faster during training phases.

Demonstrates robustness across multiple datasets.

Abstract

This paper introduces HPC-Net, a high-precision and rapidly convergent object detection network.