Research on Object Detection Algorithm Based on Improved PointPillars

To address the issue of the PointPillars backbone network’s limited feature extraction and loss of small target characteristics, a target detection algorithm called FOPointPillars is proposed as an improvement of PointPillars. Firstly, ODConv(Omni-dimensional Dynamic Convolution) is introduced to replace the general convolution method to extract features from pseudo-images, enhancing the capability to extract features. Secondly, FPN(Feature Pyramid Network) is incorporated to fuse the extracted features at multiple scales and make precise semantic information for small objects obtained. Next, the network is trained and tested using the KITTI public dataset. Finally, the network is deployed on a self-researched cart. The FOPointPillars detection algorithm achieves mAP of 70.51%, 64.31%, and 71.64% for BEV(Bird’s Eye View), 3D Space, and AOS, respectively. Compared to the original PointPillars network, the algorithm shows an increase of 1.65%, 0.74%, and 2.18% in mAP, respectively. The detection function of this method for obstacles could provide assistance in environmental perception for driverless trolleys.