Study of Dropout in PointPillars with 3D Object Detection
This work addresses overfitting for autonomous driving applications, but it is incremental as it applies an existing regularization technique to a known model.
This study tackled overfitting in the PointPillars 3D object detection model by analyzing dropout rates, finding optimal enhancements that improved generalization and performance metrics like Average Precision (AP) and Average Orientation Similarity (AOS).
3D object detection is critical for autonomous driving, leveraging deep learning techniques to interpret LiDAR data. The PointPillars architecture is a prominent model in this field, distinguished by its efficient use of LiDAR data. This study provides an analysis of enhancing the performance of PointPillars model under various dropout rates to address overfitting and improve model generalization. Dropout, a regularization technique, involves randomly omitting neurons during training, compelling the network to learn robust and diverse features. We systematically compare the effects of different enhancement techniques on the model's regression performance during training and its accuracy, measured by Average Precision (AP) and Average Orientation Similarity (AOS). Our findings offer insights into the optimal enhancements, contributing to improved 3D object detection in autonomous driving applications.