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A High-Speed NMS Coprocessor for Lightweight Ship Detection Algorithm
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AI
DOI：10.1109/TCSII.2021.3125487.png)
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With the development of artificial intelligence research, the convolutional neural networks (CNN) triumphantly continue to conquer diverse visual tasks, with lots of technical and practical problems emerging. When the detection networks need to meet the high-speed processing requirement and the low power consumption constraint in edge computing, dedicated circuits and algorithm optimization are necessary. In this brief, we propose a non-maximum suppression (NMS) coprocessor that cooperated with a Vision Processor (VP) to speed up the detection. It contains a parallel process unit and dynamic memory management to accelerate NMS processing. Meanwhile, the software workflow provides a comprehensive method to convert the one-stage detection network to our processor in a post-training manner. The experimental results show that our co-design solution reaches 166 FPS to run a MobileNet-SSD, promising to deploy in edge-computing scenarios. Moreover, the detection accuracy on the Airbus dataset is 88.6%, favorable in practical applications.
Keyword:
Quantization (signal)
Coprocessors
Decoding
Encoding
Training
Head
Software
Object detection
hardware
software co-design
neural network accelerator
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I
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4.9
论文数:
8.8K
被引数:
2.5W
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