嵌入式实时目标检测部署实战
引言
目标检测是计算机视觉的核心任务，YOLO系列模型因其高效性成为嵌入式部署的首选。本文介绍从模型训练到边缘设备部署的完整流程。
YOLO模型架构
YOLOv8核心组件
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| class YOLOv8(nn.Module):
"""YOLOv8模型架构"""
def __init__(self, num_classes=80):
super().__init__()
self.backbone = CSPDarknet()
self.neck = PANet()
self.head = YOLOv8Head(num_classes)
def forward(self, x):
features = self.backbone(x)
neck_features = self.neck(features)
outputs = self.head(neck_features)
return outputs
def detect(self, x, conf_threshold=0.25):
predictions = self.forward(x)
boxes, scores, classes = self.postprocess(predictions, conf_threshold)
return boxes, scores, classes
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模型优化
训练配置
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| training_config = {
'model': 'yolov8m.pt',
'data': 'custom_dataset.yaml',
'epochs': 100,
'batch_size': 16,
'imgsz': 640,
'optimizer': 'AdamW',
'lr0': 0.001,
'augment': True,
'mosaic': 1.0,
'mixup': 0.1
}
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数据增强
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| class DataAugmentation:
def __init__(self):
self.augmentations = [
RandomFlip(prob=0.5),
RandomCrop(prob=0.3),
ColorJitter(brightness=0.2, contrast=0.2),
Mosaic(prob=1.0),
MixUp(prob=0.1)
]
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TensorRT部署
模型转换
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|
model = YOLOv8()
model.load('best.pt')
model.eval()
torch.onnx.export(
model,
torch.randn(1, 3, 640, 640),
'yolov8m.onnx',
opset_version=11,
dynamic_axes={'input': {0: 'batch'}}
)
import tensorrt as trt
logger = trt.Logger(trt.Logger.WARNING)
builder = trt.Builder(logger)
network = builder.create_network(1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
parser = trt.OnnxParser(network, logger)
with open('yolov8m.onnx', 'rb') as f:
parser.parse(f.read())
engine = builder.build_serialized_network(network, builder.create_builder_config())
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推理引擎
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| class TensorRTInference:
def __init__(self, engine_path):
with open(engine_path, 'rb') as f:
self.engine = runtime.deserialize_cuda_engine(f.read())
self.context = self.engine.create_execution_context()
def infer(self, input_data):
d_input = cuda.mem_alloc(input_data.nbytes)
d_output = cuda.mem_alloc(input_data.nbytes)
cuda.memcpy_htod_async(d_input, input_data)
self.context.execute_async_v2([int(d_input), int(d_output)])
cuda.memcpy_dtoh_async(output, d_output)
return output
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边缘设备部署
NVIDIA Jetson
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sudo apt install tensorrt
sudo nvpmodel -m 1
python3 -c "import tensorrt; print(tensorrt.__version__)"
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性能基准
| 设备 |
精度 |
FPS |
延迟 |
| RTX 3090 |
FP16 |
150 |
6ms |
| Jetson AGX Orin |
FP16 |
80 |
12ms |
| Jetson Nano |
FP32 |
15 |
66ms |
| Raspberry Pi 4 |
INT8 |
8 |
125ms |
总结
通过模型优化和TensorRT加速，目标检测模型可以在边缘设备上实现实时推理。
参考：Ultralytics YOLOv8官方文档
