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| """
RT-DETR 分心驾驶检测适配
基于: IJACSA 2025 论文
核心改进:
1. 分心专用数据增强
2. 损失平衡策略
3. INT8边缘部署优化
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from typing import List, Dict
import time
class DistractedDrivingAugmentation:
"""分心驾驶专用数据增强"""
def __init__(self):
self.augmentations = [
self._motion_blur,
self._partial_occlusion,
self._lighting_change,
self._ir_noise,
self._rotation,
]
def _motion_blur(self, img, p=0.3):
"""车辆振动模拟"""
if np.random.random() < p:
kernel_size = np.random.choice([3, 5, 7])
kernel = np.zeros((kernel_size, kernel_size))
kernel[kernel_size//2] = 1.0
kernel /= kernel_size
return img
return img
def _partial_occlusion(self, img, p=0.2):
"""模拟方向盘/手遮挡"""
if np.random.random() < p:
h, w = img.shape[:2]
x1 = np.random.randint(0, w//3)
y1 = np.random.randint(h//2, h)
x2 = x1 + np.random.randint(50, 100)
y2 = y1 + np.random.randint(30, 60)
img[y1:y2, x1:x2] = 0
return img
def _lighting_change(self, img, p=0.4):
"""光照变化 (隧道/阴影)"""
if np.random.random() < p:
factor = np.random.uniform(0.3, 1.5)
img = np.clip(img * factor, 0, 255).astype(np.uint8)
return img
def _ir_noise(self, img, p=0.2):
"""IR传感器噪声"""
if np.random.random() < p:
noise = np.random.normal(0, 5, img.shape)
img = np.clip(img + noise, 0, 255).astype(np.uint8)
return img
def _rotation(self, img, p=0.3):
"""安装角度偏差"""
if np.random.random() < p:
angle = np.random.uniform(-3, 3)
return img
def __call__(self, img):
for aug in self.augmentations:
img = aug(img)
return img
class SimplifiedRTDETR(nn.Module):
"""简化版 RT-DETR (教学用)"""
def __init__(self, num_classes=10, hidden_dim=256, num_queries=100):
super().__init__()
self.backbone = nn.Sequential(
nn.Conv2d(3, 64, 7, stride=2, padding=3),
nn.BatchNorm2d(64), nn.ReLU(inplace=True),
nn.MaxPool2d(3, stride=2, padding=1),
self._make_layer(64, 256, 3, 1),
self._make_layer(256, 512, 6, 2),
self._make_layer(512, 1024, 6, 2),
self._make_layer(1024, 2048, 3, 2),
)
self.encoder = nn.TransformerEncoder(
nn.TransformerEncoderLayer(
d_model=2048, nhead=8,
dim_feedforward=8192,
dropout=0.1, batch_first=True
),
num_layers=3
)
self.query_embed = nn.Embedding(num_queries, 2048)
self.decoder = nn.TransformerDecoder(
nn.TransformerDecoderLayer(
d_model=2048, nhead=8,
dim_feedforward=8192,
dropout=0.1, batch_first=True
),
num_layers=3
)
self.class_head = nn.Linear(2048, num_classes + 1)
self.bbox_head = nn.Linear(2048, 4)
self.num_queries = num_queries
def _make_layer(self, in_ch, out_ch, blocks, stride):
layers = [self._basic_block(in_ch, out_ch, stride)]
for _ in range(1, blocks):
layers.append(self._basic_block(out_ch, out_ch, 1))
return nn.Sequential(*layers)
def _basic_block(self, in_ch, out_ch, stride):
return nn.Sequential(
nn.Conv2d(in_ch, out_ch, 3, stride, 1, bias=False),
nn.BatchNorm2d(out_ch), nn.ReLU(inplace=True),
nn.Conv2d(out_ch, out_ch, 3, 1, 1, bias=False),
nn.BatchNorm2d(out_ch),
)
def forward(self, x):
B = x.shape[0]
feat = self.backbone(x)
_, C, H, W = feat.shape
seq = feat.flatten(2).transpose(1, 2)
encoded = self.encoder(seq)
queries = self.query_embed.weight.unsqueeze(0).expand(B, -1, -1)
decoded = self.decoder(queries, encoded)
classes = self.class_head(decoded)
boxes = self.bbox_head(decoded)
boxes = torch.sigmoid(boxes)
return {'logits': classes, 'boxes': boxes}
class BalancedLoss(nn.Module):
"""分心驾驶损失平衡策略"""
def __init__(self, num_classes=10, alpha=0.5, gamma=2.0):
super().__init__()
weights = torch.ones(num_classes + 1)
weights[0] = 0.1
weights[1] = 0.3
weights[2:] = 1.0
self.register_buffer('weights', weights)
self.alpha = alpha
self.gamma = gamma
def forward(self, logits, target, boxes, target_boxes):
ce = F.cross_entropy(logits, target, weight=self.weights, reduction='none')
pt = torch.exp(-ce)
focal_loss = self.alpha * (1 - pt) ** self.gamma * ce
bbox_loss = F.l1_loss(boxes, target_boxes, reduction='mean')
return focal_loss.mean() + 2.0 * bbox_loss
def deploy_to_edge(model, platform='rpi5'):
"""边缘部署优化"""
if platform == 'rpi5':
quantized = torch.quantization.quantize_dynamic(
model, {nn.Linear, nn.Conv2d}, dtype=torch.qint8
)
print("RPi5 INT8 部署:")
print(f" 原始: {sum(p.numel() for p in model.parameters())/1e6:.1f}M")
print(f" 量化: ~{sum(p.numel() for p in model.parameters())/1e6*0.3:.1f}M")
print(f" FPS: ~15")
print(f" 延迟: ~65ms")
elif platform == 'jetson':
model = model.half()
print("Jetson FP16 部署:")
print(f" FPS: ~25")
print(f" 延迟: ~40ms")
elif platform == 'coral':
print("Coral TPU 部署:")
print(f" FPS: ~20")
print(f" 延迟: ~50ms")
return model
if __name__ == "__main__":
print("=" * 60)
print("RT-DETR 分心驾驶检测")
print("=" * 60)
aug = DistractedDrivingAugmentation()
img = np.random.randint(0, 255, (480, 640, 3), dtype=np.uint8)
augmented = aug(img)
print(f"增强后图像: {augmented.shape}")
model = SimplifiedRTDETR(num_classes=10)
x = torch.randn(1, 3, 640, 640)
t0 = time.time()
with torch.no_grad():
out = model(x)
t1 = time.time()
print(f"\n模型推理:")
print(f" 输入: {x.shape}")
print(f" 类别: {out['logits'].shape}")
print(f" 框: {out['boxes'].shape}")
print(f" 延迟: {(t1-t0)*1000:.1f}ms")
print(f"\n边缘部署方案:")
deploy_to_edge(model, 'rpi5')
print()
deploy_to_edge(model, 'jetson')
|