Adaptive Feature Aggregation Centric Enhance Network for Accurate and Fast Monocular 3-D Object Detection

Three-dimensional object detection is crucial in autonomous driving. Monocular 3-D object detection has become a popular area of research in autonomous driving because of its ease of deployment and cost-effectiveness. In real-world autonomous driving, detectors should be both real time and accurate. These features can be achieved using deep learning (DL). A one-stage center-based object detector is suitable for real-world applications. However, in center-based object detectors, object-centric estimation plays an important role because it significantly influences detection results. To address this issue, we propose a real-time monocular 3-D object detection neural network called the adaptive feature aggregate centric enhance network. The proposed model is an anchor-free and center-based method. To improve accuracy while maintaining inference speed, we propose an adaptive feature aggregation network that aggregates multiscale features by weighting. Furthermore, we propose a centric enhanced module for heatmap prediction to improve the accuracy of object localization and classification. Our model can achieve 34.48 fps using an Nvidia RTX3070 graphy processing unit (GPU). Extensive experiments on the KITTI benchmark demonstrate that our method achieves good average precision (AP) for cars and pedestrians.