Multiple object detection of workpieces based on fusion of deep learning and image processing

A workpiece detection method based on fusion of deep learning and image processing is proposed. Firstly, the workpiece bounding boxes are located in the workpiece images by YOLOv3, whose parameters are compressed by an improved convolutional neural network residual structure pruning strategy. Then, the workpiece images are cropped based on the bounding boxes with cropping biases. Finally, the contours and suitable gripping points of the workpieces are obtained through image processing. The experimental results show that mean Average Precision (mAP) is 98.60% for YOLOv3, and 99.38% for that one by pruning 50.89% of its parameters, and the inference time is shortened by 31.13%. Image processing effectively corrects the bounding boxes obtained by deep learning, and obtains workpiece contour and gripping point information

From CAD models to soft point cloud labels: An automatic annotation pipeline for cheaply supervised 3D semantic segmentation

We propose a fully automatic annotation scheme which takes a raw 3D point cloud with a set of fitted CAD models as input, and outputs convincing point-wise labels which can be used as cheap training data for point cloud segmentation. Compared to manual annotations, we show that our automatic labels are accurate while drastically reducing the annotation time, and eliminating the need for manual intervention or dataset-specific parameters. Our labeling pipeline outputs semantic classes and soft point-wise object scores which can either be binarized into standard one-hot-encoded labels, thresholded into weak labels with ambiguous points left unlabeled, or used directly as soft labels during training. We evaluate the label quality and segmentation performance of PointNet++ on a dataset of real industrial point clouds and Scan2CAD, a public dataset of indoor scenes. Our results indicate that reducing supervision in areas which are more difficult to label automatically is beneficial, compared to the conventional approach of naively assigning a hard "best guess" label to every point