Hardware-Aware Design of Speed-Up Algorithms for Synthetic Aperture Radar Ship Target Detection Networks

Recently, synthetic aperture radar (SAR) target detection algorithms based on Convolutional Neural Networks (CNN) have received increasing attention. However, the large amount of computation required burdens the real-time detection of SAR ship targets on resource-limited and power-constrained satell...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Vol. 15; no. 20; p. 4995
Main Authors: Zhang, Yue, Jiang, Shuai, Cao, Yue, Xiao, Jiarong, Li, Chengkun, Zhou, Xuan, Yu, Zhongjun
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.10.2023
Subjects:
Acceleration
Accuracy
Algorithms
Artificial intelligence
Artificial neural networks
Comparative analysis
Compressing
convolutional neural networks (CNN)
data collection
graphic processing unit (GPU)
Graphics processing units
Hardware
Identification and classification
Information processing
model speed-up algorithms
Neural networks
Parameter estimation
Radar
Radar detection
Remote sensing
satellites
Ships
Statistical methods
Synthetic aperture radar
synthetic aperture radar (SAR)
Target detection
China
ISSN:2072-4292, 2072-4292
Online Access:Get full text
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Summary:Recently, synthetic aperture radar (SAR) target detection algorithms based on Convolutional Neural Networks (CNN) have received increasing attention. However, the large amount of computation required burdens the real-time detection of SAR ship targets on resource-limited and power-constrained satellite-based platforms. In this paper, we propose a hardware-aware model speed-up method for single-stage SAR ship targets detection tasks, oriented towards the most widely used hardware for neural network computing—Graphic Processing Unit (GPU). We first analyze the process by which the task of detection is executed on GPUs and propose two strategies according to this process. Firstly, in order to speed up the execution of the model on a GPU, we propose SAR-aware model quantification to allow the original model to be stored and computed in a low-precision format. Next, to ensure the loss of accuracy is negligible after the acceleration and compression process, precision-aware scheduling is used to filter out layers that are not suitable for quantification and store and execute them in a high-precision mode. Trained on the dataset HRSID, the effectiveness of this model speed-up algorithm was demonstrated by compressing four different sizes of models (yolov5n, yolov5s, yolov5m, yolov5l). The experimental results show that the detection speeds of yolov5n, yolov5s, yolov5m, and yolov5l can reach 234.7785 fps, 212.8341 fps, 165.6523 fps, and 139.8758 fps on the NVIDIA AGX Xavier development board with negligible loss of accuracy, which is 1.230 times, 1.469 times, 1.955 times, and 2.448 times faster than the original before the use of this method, respectively.
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ISSN:2072-4292
2072-4292
DOI:10.3390/rs15204995