SODet: A LiDAR-Based Object Detector in Bird’s-Eye View

LiDAR-based object detection is of paramount significance in the realm of autonomous driving applications. Nevertheless, the detection of small objects from a bird’s-eye view perspective remains challenging. To address this issue, the paper presents SODet, an efficient single-stage 3D object detector designed to enhance the perception of small objects like pedestrians and cyclists. SODet incorporates several key components and techniques. To capture broader context information and augment the capability of feature representation, the model constructs residual blocks comprising large-kernel depthwise convolutions and inverted bottleneck structures, forming the foundation of the CSP-based NeXtDark backbone network. Furthermore, the NeXtFPN feature extraction network is designed with the introduced SPPF module and the proposed special residual blocks, enabling the extraction and fusion of multi-scale information. Additionally, training strategies such as mosaic data augmentation and cosine annealing learning rate are employed to further improve small object detection accuracy. The effectiveness of SODet is demonstrated through experimental results on the KITTI dataset, showcasing a remarkable enhancement in detecting small objects from a bird’s-eye perspective while maintaining a detection speed of 20.6 FPS.