PIAENet: Pyramid integration and attention enhanced network for object detection

Abstract

Object detection is a challenging task that requires a trade-off between accuracy and efficiency. Previous approaches have focused mainly on optimizing one aspect at the expense of the other, making them unsuitable for resource-constrained devices. To address this issue, we propose a new object detection network architecture, the Pyramid Integration and Attention Enhanced Network (PIAENet). PIAENet is a lightweight architecture that can achieve high accuracy and efficiency. We utilize a lightweight EfficientNet-B2 backbone for feature extraction to maintain accuracy while reducing computational overhead. The core components of PIAENet, the Pyramid Integration Module (PIM) and the Attention Enhanced Module (AEM), work together to improve the performance of object detection. PIM fuses multi-scale features using multiple branches to enhance the receptive field of the model, while AEM strengthens the fusion of features using two attention mechanisms to suppress the influence of irrelevant information. Our proposed method has been evaluated on the PASCAL VOC and KITTI datasets. The results have shown our method outperforms most of the existing state-of-the-art methods.