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| """
IMS 边缘部署算力需求分析
功能模块:
1. 驾驶员检测 (DMS): 人脸检测+关键点+疲劳+分心
2. 乘员检测 (OMS): 姿态+CPD+安全带
3. 多传感器融合: 雷达+摄像头
4. 全景感知: 周视摄像头
"""
import numpy as np
ims_modules = {
'DMS - 人脸检测 (YOLOv8n-face)': {
'model_size_mb': 6,
'gops_per_frame': 8.5,
'fps': 30,
'total_tops': 0.26,
'latency_ms': 5
},
'DMS - 人脸关键点 (68点)': {
'model_size_mb': 2,
'gops_per_frame': 1.2,
'fps': 30,
'total_tops': 0.04,
'latency_ms': 2
},
'DMS - 疲劳检测 (PERCLOS+时序)': {
'model_size_mb': 5,
'gops_per_frame': 2.0,
'fps': 30,
'total_tops': 0.06,
'latency_ms': 3
},
'DMS - 分心检测 (视线+头姿)': {
'model_size_mb': 8,
'gops_per_frame': 3.5,
'fps': 30,
'total_tops': 0.11,
'latency_ms': 4
},
'OMS - 乘员姿态 (HRNet)': {
'model_size_mb': 15,
'gops_per_frame': 12.0,
'fps': 15,
'total_tops': 0.18,
'latency_ms': 8
},
'OMS - CPD (雷达处理)': {
'model_size_mb': 3,
'gops_per_frame': 0.8,
'fps': 10,
'total_tops': 0.008,
'latency_ms': 5
},
'OMS - 安全带检测': {
'model_size_mb': 4,
'gops_per_frame': 2.5,
'fps': 10,
'total_tops': 0.025,
'latency_ms': 6
},
'融合 - 多传感器': {
'model_size_mb': 10,
'gops_per_frame': 5.0,
'fps': 30,
'total_tops': 0.15,
'latency_ms': 5
},
}
print("=" * 75)
print("IMS 边缘部署算力需求分析")
print("=" * 75)
total_tops = 0
total_size = 0
max_latency = 0
print(f"\n{'模块':<30s} {'大小':>6s} {'GOPS/帧':>8s} {'FPS':>5s} {'TOPS':>7s} {'延迟':>6s}")
print("-" * 65)
for name, spec in ims_modules.items():
print(f"{name:<30s} {spec['model_size_mb']:>5}M {spec['gops_per_frame']:>7.1f} "
f"{spec['fps']:>4d} {spec['total_tops']:>6.3f} {spec['latency_ms']:>5}ms")
total_tops += spec['total_tops']
total_size += spec['model_size_mb']
max_latency = max(max_latency, spec['latency_ms'])
print("-" * 65)
print(f"{'总计':<30s} {total_size:>5}M {'':>8s} {'':>5s} {total_tops:>6.3f} {max_latency:>5}ms")
print(f"\n所需 NPU 算力: {total_tops:.2f} TOPS")
print(f"模型总大小: {total_size} MB")
print(f"最大单模块延迟: {max_latency} ms")
print(f"端到端管道延迟 (串行): ~{sum(s['latency_ms'] for s in ims_modules.values())} ms")
print(f"端到端管道延迟 (并行): ~{max_latency} ms")
print(f"\n{'='*75}")
print("平台适配分析")
print(f"{'='*75}")
platforms = [
('QCS8255', 26, 5, '车载标准'),
('IQ8', 40, 12, '工业+车载'),
('IQ9', 100, 20, '高性能边缘'),
('Jetson Orin NX', 70, 25, '通用边缘'),
('i.MX 95 (EP-200N)', 8, 6, '低功耗嵌入'),
]
print(f"\n{'平台':<22s} {'TOPS':>6s} {'功耗W':>6s} {'IMS可行':>8s} {'余量':>8s} {'定位':<15s}")
print("-" * 70)
for name, tops, power, label in platforms:
feasible = tops >= total_tops
margin = tops - total_tops
status = '✅' if feasible else '❌'
margin_str = f"+{margin:.1f}" if margin >= 0 else f"{margin:.1f}"
print(f"{name:<22s} {tops:>5d} {power:>5d} {status:>8s} {margin_str:>7s}T {label:<15s}")
print(f"\n{'='*75}")
print("推荐部署方案")
print(f"{'='*75}")
scenarios = [
{
'name': '基础 DMS (仅驾驶员)',
'modules': ['DMS - 人脸检测 (YOLOv8n-face)', 'DMS - 人脸关键点 (68点)',
'DMS - 疲劳检测 (PERCLOS+时序)', 'DMS - 分心检测 (视线+头姿)'],
'platform': 'QCS8255 (26 TOPS)',
},
{
'name': 'DMS + OMS (全座舱)',
'modules': list(ims_modules.keys())[:7],
'platform': 'IQ8 (40 TOPS)',
},
{
'name': '全功能 IMS (含融合)',
'modules': list(ims_modules.keys()),
'platform': 'IQ9 (100 TOPS)',
}
]
for sc in scenarios:
sc_tops = sum(ims_modules[m]['total_tops'] for m in sc['modules'])
sc_size = sum(ims_modules[m]['model_size_mb'] for m in sc['modules'])
print(f"\n {sc['name']}:")
print(f" 所需算力: {sc_tops:.2f} TOPS")
print(f" 模型大小: {sc_size} MB")
print(f" 推荐平台: {sc['platform']}")
|
