MNN Optimization Scheme for Industrial Vision Scenarios
For industrial quality control and other scenarios that require real-time image processing:
- Use of MNN-CV: Lightweight Image Processing Library Replaces OpenCV
- Flow line optimization:: Parallelization of pre-processing, reasoning and post-processing phases
- hardware acceleration:: Leverage the device's NEON instruction set and GPU compute unit
- frame rate control: Adjustment of balancing accuracy and speed by dynamic resolution
Key technology realization:
1. Example of image preprocessing code:
MNN::CV::ImageProcess::Config config;
config.sourceFormat = MNN::CV::BGR;
auto pretreat = MNN::CV::ImageProcess::create(config);
2. Performance monitoring indicators:
- Single-frame processing time (<5ms preferred)
- CPU/GPU utilization
- peak memory usage
This answer comes from the articleMNN: A Lightweight and Efficient Deep Learning Inference FrameworkThe
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