feat: initial HSAP platform

Huaxu Sentinel Active Safety Platform with embedded algorithm code,
Docker Compose setup, and vendored dataset scaffolds for clone-and-run.

Co-authored-by: Cursor <cursoragent@cursor.com>

platform/as_platform/data/ingest/dms_yolo.py

@@ -0,0 +1,67 @@
+"""DMS YOLO-style dataset adapter."""
+from __future__ import annotations
+
+from pathlib import Path
+
+from as_platform.data.ingest.base import IngestAdapter, IngestContext, NormalizedDataset
+
+IMG_EXTS = {".jpg", ".jpeg", ".png", ".bmp", ".webp", ".JPG", ".JPEG", ".PNG"}
+
+
+def _count_images(path: Path) -> int:
+    if not path.is_dir():
+        return 0
+    return sum(1 for p in path.rglob("*") if p.is_file() and p.suffix in IMG_EXTS)
+
+
+def _count_txt(path: Path) -> int:
+    if not path.is_dir():
+        return 0
+    return sum(1 for p in path.rglob("*.txt") if p.is_file())
+
+
+class DmsYoloAdapter(IngestAdapter):
+    format_id = "dms_yolo"
+    projects = ("dms",)
+
+    def can_handle(self, ctx: IngestContext) -> bool:
+        root = ctx.source_path
+        return (
+            (root / "images").is_dir()
+            and (root / "labels").is_dir()
+        ) or (
+            (root / "images" / "train").is_dir()
+            and (root / "labels" / "train").is_dir()
+        )
+
+    def inspect(self, ctx: IngestContext) -> NormalizedDataset:
+        root = ctx.source_path
+        train_images = _count_images(root / "images" / "train")
+        val_images = _count_images(root / "images" / "val")
+        test_images = _count_images(root / "images" / "test")
+        if train_images + val_images + test_images == 0:
+            # fallback single-folder dataset
+            train_images = _count_images(root / "images")
+        train_labels = _count_txt(root / "labels" / "train")
+        val_labels = _count_txt(root / "labels" / "val")
+        test_labels = _count_txt(root / "labels" / "test")
+        if train_labels + val_labels + test_labels == 0:
+            train_labels = _count_txt(root / "labels")
+
+        warnings: list[str] = []
+        if train_images == 0:
+            warnings.append("train split has no images")
+        if train_labels == 0:
+            warnings.append("train split has no labels")
+
+        return NormalizedDataset(
+            format_id=self.format_id,
+            project=ctx.project,
+            task=ctx.task,
+            source_path=str(root),
+            split_counts={"train": train_images, "val": val_images, "test": test_images},
+            sample_count=train_images + val_images + test_images,
+            annotation_count=train_labels + val_labels + test_labels,
+            artifacts=["images/", "labels/"],
+            warnings=warnings,
+        )