3D Object Detection Using Lidar Data for Self Driving Cars

3D Object Detection Using Lidar Data for Self Driving Cars

New state of the art neural network for 3D Object Detection. In this blog, we present our research work on 3D Object Detection in real time using lidar data.

In this blog, we present our research work on 3D Object Detection in real time using lidar data.

Important Points

  1. A novel neural network architecture is used to simultaneously detect and regress bounding box over all the objects present in the image.
  2. We used 2D Bird’s Eye View in place of 3D voxel grid data because it is much less computationally heavy. This will also make our detector to be easily deployed to real work settings especially in case of self driving cars.
  3. We compare the results with different backbone architectures including the standard ones like VGG, ResNet, Inception with our backbone.
  4. We show the optimization and ablation studies including designing an efficient anchor.
  5. We use the Kitti 3D Bird’s Eye View dataset for benchmarking and evaluating our results.
  6. Our work surpasses the previous state of the art both in terms of average precision while still running at > 30 FPS.

2D Object Detection

The 2D object detection algorithms can be broadly grouped into the following two types:

  1. Single stage detector — Yolo and SSD.
  2. Two stage detector — RCNN, Fast RCNN and Faster RCNN.

The difference between the two is that in the two stage detectors, the first stage uses region proposal networks to generate regions of interest and the second stage uses these regions of interest for object classification and bounding box regression. On the other hand, a single stage detector uses the input image to directly learn the class wise probability and bounding box coordinates. Thus these architectures treat the object detection as a simple regression problem and thus are faster but less accurate.

artificial-intelligence computer-vision deep-learning neural-networks machine-learning deep learning

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