FPGA-Based Hardware Accelerator Design and Implementation of Oil Palm Detection

• Published in: Jisuanji kexue yu tansuo Vol. 15; no. 2; pp. 315 - 326
• Main Author: YUAN Ming, CHAI Zhilei, GAN Lin
• Format: Journal Article
• Language: Chinese
• Published: Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press 01.02.2021
• Subjects: field-programmable gate array (fpga); hardware accelerator; improved yolov3; oil palm
• ISSN: 1673-9418
• DOI: 10.3778/j.issn.1673-9418.1912029

Aiming at the problems of low accuracy and low detection efficiency of high-resolution oil palm detection in deep learning, an effective and reliable solution is proposed from two aspects of algorithm optimization and heterogeneous hardware platform acceleration. Taking YOLOv3 object detection algorithm as an example, the optimization strategy of expanding feature selection and increasing multi-scale feature fusion is adopted to improve the detection accuracy of the algorithm for high-resolution oil palm. In addition, in the process of inference, plenty of applications require high performance models with strict power consumption limits. In order to solve this problem, taking the strategy of integer 8-bits quantitative weights and computational units reuse, this paper designs a high efficiency convolution computational engine based on SIMD. At the same time, through the strategy of the dimension change of the input image, vectorization, transmission to the input module in the form of written queue, this paper