A very high frame rate camera is designed based on an innovative CCD driving method. The CCD driving method is mainly implemented on frame transfer CCDs. Asynchronous drive timing sequences are applied in the image and storage section of the CCDs. Several rows of the charge in the image section are binned onto the same row in the storage section, and there are the same number of images to be stored in the storage section before they are read out. Based on the new driving method, the frame transfer CCDs can work at a very high frame rate in acquiring burst images though the reading speed remains at a lower level. A very high frame rate camera is designed in this paper. The innovative CCD driving method is mainly of concern. An e2v's CCD60 is adopted in the camera system, whose full size resolution is 128×128, and the up most frame rate is 1000 Hz in the conventional CCD driving method. By using the presented method, the CCD60 based imager is capable of operating at up to 40000 frames per second (fps) at a recognizable resolution of 128×32. Comparing cameras using traditional binning and region of interest technologies, the frame rate is normally less than 5000 fps while the resolution is only 32×32 left.
Grasp detection takes on a critical significance for the robot. However, detecting object positions and corresponding grasp positions in a stacked environment can be quite difficult for a robot. Based on this practical problem, in order to achieve more accurate object position detection and grasp position detection, a new method called MMD (Multi-stage network for multi-object grasp detection algorithm) is proposed in this paper. MMD covers two parts, including the feature extractor and the multi-stage object predictor. The feature extractor refers to a deep convolutional neural network that can generate shared feature layers as well as the initial ROIs (region of interest). A multi-stage refiner serves as the multi-stage object predictor, which continuously regresses the initial ROI to obtain more accurate object detection and grasping detection results. Ablation experiments show that the proposed MMD has better grasp detection performance. The specific performance is that the recognition precision achieves a state-of-the-art 76.71% mAPg on the VMRD dataset. Moreover, test experiments demonstrate the feasibility of our method on the Kinova robot.
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