KEYWORDS: Object detection, Field programmable gate arrays, Power consumption, Convolution, Design, Digital signal processing, Neural networks, Parallel computing
Under the premise of ensuring network performance, ResNet proposes shortcut connections as the basic structure of the network to self-adjust the depth of the network to converge to the identity map. This resolves the performance degradation that occurs after the number of network layers becomes deeper. Field Programmable Gate Array (FPGA) is a highly flexible hardware platform, which is more convenient for the deployment of different neural network structures. Therefore, this paper accelerates ResNet object detection algorithm based on the FPGA platform. Firstly, the ping-pong buffer mechanism ensures data synchronization and correctness in parallel computing, so that the intermediate feature maps are alternately cached within the clock cycle. Secondly, it implements loop unrolling execution of computing tasks at each stage in the pipeline, which made full use of hardware resources and improved computing throughput. In addition, due to the limited resources of FPGA, operator fusion was combined to reduce the power consumption by reducing the number of reads and writes to the cache by the network. Although the accuracy of ResNet acceleration based on the FPGA platform is reduced, the detection speed is increased, and there are certain advantages in performance.
KEYWORDS: Point clouds, Calibration, Image segmentation, Cameras, 3D modeling, 3D image reconstruction, Image registration, Convolution, Image processing, Camera calibration
The pipeline welding robot is limited by mechanization and programmed operation, and it is difficult to adjust the welding mode according to the actual situation of the pipeline. It has the disadvantages of long welding teaching cycle and complicated manual operation. The welding quality of the pipeline is generally low, and the basic stability of the production quality is difficult to guarantee. Based on the research of machine vision, three-dimensional scanning of welds is carried out to obtain three-dimensional point cloud data to achieve high-precision three-dimensional reconstruction of pipeline welds, which can simplify the operation process, reduce labor costs, improve production quality, and make welding robots adapt to the development needs of flexible manufacturing field. Therefore, a set of welding robot system is designed. In this paper, the depth map is obtained by SGBM, and the camera pose is obtained and optimized by the image pose + point cloud registration method to realize the three-dimensional reconstruction of the pipeline. Based on the semantic segmentation of pipeline weld images in complex scenes and the three-dimensional automatic measurement of pipeline reconstruction welds, welds were obtained based on the semantic segmentation model of lightweight backbone network Mobilenetv2+Unet, and the width of welds was measured by the centerline method.
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