diff --git a/frigate/util/rknn_converter.py b/frigate/util/rknn_converter.py
index 48ce3e0b1..3317cb34f 100644
--- a/frigate/util/rknn_converter.py
+++ b/frigate/util/rknn_converter.py
@@ -1,15 +1,16 @@
 """RKNN model conversion utility for Frigate."""
 
+import fcntl
 import logging
 import os
 import subprocess
 import sys
+import time
 from pathlib import Path
-from typing import Dict, Any, Optional
+from typing import Optional
 
 logger = logging.getLogger(__name__)
 
-# Model type to RKNN conversion config mapping
 MODEL_TYPE_CONFIGS = {
     "yolo-generic": {
         "mean_values": [[0, 0, 0]],
@@ -32,7 +33,7 @@ MODEL_TYPE_CONFIGS = {
 def ensure_torch_dependencies() -> bool:
     """Dynamically install torch dependencies if not available."""
     try:
-        import torch
+        import torch  # type: ignore
 
         logger.debug("PyTorch is already available")
         return True
@@ -40,7 +41,6 @@ def ensure_torch_dependencies() -> bool:
         logger.info("PyTorch not found, attempting to install...")
 
         try:
-            # Try to install torch using pip
             subprocess.check_call(
                 [
                     sys.executable,
@@ -55,7 +55,6 @@ def ensure_torch_dependencies() -> bool:
                 stderr=subprocess.DEVNULL,
             )
 
-            # Verify installation
             import torch
 
             logger.info("PyTorch installed successfully")
@@ -68,8 +67,8 @@ def ensure_torch_dependencies() -> bool:
 def ensure_rknn_toolkit() -> bool:
     """Ensure RKNN toolkit is available."""
     try:
-        import rknn
-        from rknn.api import RKNN
+        import rknn  # type: ignore  # noqa: F401
+        from rknn.api import RKNN  # type: ignore # noqa: F401
 
         logger.debug("RKNN toolkit is already available")
         return True
@@ -109,7 +108,6 @@ def convert_onnx_to_rknn(
     Returns:
         True if conversion successful, False otherwise
     """
-    # Ensure dependencies are available
     if not ensure_torch_dependencies():
         logger.error("PyTorch dependencies not available")
         return False
@@ -137,24 +135,17 @@ def convert_onnx_to_rknn(
         from rknn.api import RKNN
 
         logger.info(f"Converting {onnx_path} to RKNN format for {soc}")
-
-        # Initialize RKNN
         rknn = RKNN(verbose=True)
-
-        # Configure RKNN
         rknn.config(**config)
 
-        # Load ONNX model
         if rknn.load_onnx(model=onnx_path) != 0:
             logger.error("Failed to load ONNX model")
             return False
 
-        # Build RKNN model
         if rknn.build(do_quantization=quantization) != 0:
             logger.error("Failed to build RKNN model")
             return False
 
-        # Export RKNN model
         if rknn.export_rknn(output_path) != 0:
             logger.error("Failed to export RKNN model")
             return False
@@ -167,6 +158,185 @@ def convert_onnx_to_rknn(
         return False
 
 
+def cleanup_stale_lock(lock_file_path: Path) -> bool:
+    """
+    Clean up a stale lock file if it exists and is old.
+
+    Args:
+        lock_file_path: Path to the lock file
+
+    Returns:
+        True if lock was cleaned up, False otherwise
+    """
+    try:
+        if lock_file_path.exists():
+            # Check if lock file is older than 10 minutes (stale)
+            lock_age = time.time() - lock_file_path.stat().st_mtime
+            if lock_age > 600:  # 10 minutes
+                logger.warning(
+                    f"Removing stale lock file: {lock_file_path} (age: {lock_age:.1f}s)"
+                )
+                lock_file_path.unlink()
+                return True
+    except Exception as e:
+        logger.error(f"Error cleaning up stale lock: {e}")
+
+    return False
+
+
+def acquire_conversion_lock(lock_file_path: Path, timeout: int = 300) -> bool:
+    """
+    Acquire a file-based lock for model conversion.
+
+    Args:
+        lock_file_path: Path to the lock file
+        timeout: Maximum time to wait for lock in seconds
+
+    Returns:
+        True if lock acquired, False if timeout or error
+    """
+    try:
+        lock_file_path.parent.mkdir(parents=True, exist_ok=True)
+        cleanup_stale_lock(lock_file_path)
+        lock_fd = os.open(lock_file_path, os.O_CREAT | os.O_RDWR)
+
+        # Try to acquire exclusive lock
+        start_time = time.time()
+        while time.time() - start_time < timeout:
+            try:
+                fcntl.flock(lock_fd, fcntl.LOCK_EX | fcntl.LOCK_NB)
+                # Lock acquired successfully
+                logger.debug(f"Acquired conversion lock: {lock_file_path}")
+                return True
+            except (OSError, IOError):
+                # Lock is held by another process, wait and retry
+                if time.time() - start_time >= timeout:
+                    logger.warning(
+                        f"Timeout waiting for conversion lock: {lock_file_path}"
+                    )
+                    os.close(lock_fd)
+                    return False
+
+                logger.debug("Waiting for conversion lock to be released...")
+                time.sleep(1)
+
+        os.close(lock_fd)
+        return False
+
+    except Exception as e:
+        logger.error(f"Error acquiring conversion lock: {e}")
+        return False
+
+
+def release_conversion_lock(lock_file_path: Path) -> None:
+    """
+    Release the conversion lock.
+
+    Args:
+        lock_file_path: Path to the lock file
+    """
+    try:
+        if lock_file_path.exists():
+            lock_file_path.unlink()
+            logger.debug(f"Released conversion lock: {lock_file_path}")
+    except Exception as e:
+        logger.error(f"Error releasing conversion lock: {e}")
+
+
+def is_lock_stale(lock_file_path: Path, max_age: int = 600) -> bool:
+    """
+    Check if a lock file is stale (older than max_age seconds).
+
+    Args:
+        lock_file_path: Path to the lock file
+        max_age: Maximum age in seconds before considering lock stale
+
+    Returns:
+        True if lock is stale, False otherwise
+    """
+    try:
+        if lock_file_path.exists():
+            lock_age = time.time() - lock_file_path.stat().st_mtime
+            return lock_age > max_age
+    except Exception:
+        pass
+
+    return False
+
+
+def wait_for_conversion_completion(
+    rknn_path: Path, lock_file_path: Path, timeout: int = 300
+) -> bool:
+    """
+    Wait for another process to complete the conversion.
+
+    Args:
+        rknn_path: Path to the expected RKNN model
+        lock_file_path: Path to the lock file to monitor
+        timeout: Maximum time to wait in seconds
+
+    Returns:
+        True if RKNN model appears, False if timeout
+    """
+    start_time = time.time()
+    while time.time() - start_time < timeout:
+        # Check if RKNN model appeared
+        if rknn_path.exists():
+            logger.info(f"RKNN model appeared: {rknn_path}")
+            return True
+
+        # Check if lock file is gone (conversion completed or failed)
+        if not lock_file_path.exists():
+            logger.info("Lock file removed, checking for RKNN model...")
+            if rknn_path.exists():
+                logger.info(f"RKNN model found after lock removal: {rknn_path}")
+                return True
+            else:
+                logger.warning(
+                    "Lock file removed but RKNN model not found, conversion may have failed"
+                )
+                return False
+
+        # Check if lock is stale
+        if is_lock_stale(lock_file_path):
+            logger.warning(f"Lock file is stale, attempting to clean up and retry...")
+            cleanup_stale_lock(lock_file_path)
+            # Try to acquire lock again
+            if acquire_conversion_lock(lock_file_path, timeout=60):
+                try:
+                    # Check if RKNN file appeared while waiting
+                    if rknn_path.exists():
+                        logger.info(f"RKNN model appeared while waiting: {rknn_path}")
+                        return str(rknn_path)
+
+                    # Convert ONNX to RKNN
+                    logger.info(
+                        f"Retrying conversion of {rknn_path} after stale lock cleanup..."
+                    )
+
+                    # Get the original model path from rknn_path
+                    base_path = rknn_path.parent / rknn_path.stem
+                    onnx_path = base_path.with_suffix(".onnx")
+
+                    if onnx_path.exists():
+                        if convert_onnx_to_rknn(
+                            str(onnx_path), str(rknn_path), "yolo-generic", False
+                        ):
+                            return str(rknn_path)
+
+                    logger.error("Failed to convert model after stale lock cleanup")
+                    return None
+
+                finally:
+                    release_conversion_lock(lock_file_path)
+
+        logger.debug("Waiting for RKNN model to appear...")
+        time.sleep(1)
+
+    logger.warning(f"Timeout waiting for RKNN model: {rknn_path}")
+    return False
+
+
 def auto_convert_model(
     model_path: str, model_type: str, quantization: bool = False
 ) -> Optional[str]:
@@ -181,16 +351,12 @@ def auto_convert_model(
     Returns:
         Path to the RKNN model if successful, None otherwise
     """
-    from frigate.const import MODEL_CACHE_DIR
-
-    # Check if model already has .rknn extension
     if model_path.endswith(".rknn"):
         return model_path
 
     # Check if equivalent .rknn file exists
     base_path = Path(model_path)
     if base_path.suffix.lower() in [".onnx", ""]:
-        # Remove extension if present
         base_name = base_path.stem if base_path.suffix else base_path.name
         rknn_path = base_path.parent / f"{base_name}.rknn"
 
@@ -198,19 +364,38 @@ def auto_convert_model(
             logger.info(f"Found existing RKNN model: {rknn_path}")
             return str(rknn_path)
 
-        # Convert ONNX to RKNN
-        if base_path.suffix.lower() == ".onnx" or not base_path.suffix:
-            logger.info(f"Converting {model_path} to RKNN format...")
+        lock_file_path = base_path.parent / f"{base_name}.conversion.lock"
 
-            # Create output directory if it doesn't exist
-            rknn_path.parent.mkdir(parents=True, exist_ok=True)
+        if acquire_conversion_lock(lock_file_path):
+            try:
+                if rknn_path.exists():
+                    logger.info(
+                        f"RKNN model appeared while waiting for lock: {rknn_path}"
+                    )
+                    return str(rknn_path)
 
-            if convert_onnx_to_rknn(
-                str(base_path), str(rknn_path), model_type, quantization
-            ):
+                logger.info(f"Converting {model_path} to RKNN format...")
+                rknn_path.parent.mkdir(parents=True, exist_ok=True)
+
+                if convert_onnx_to_rknn(
+                    str(base_path), str(rknn_path), model_type, quantization
+                ):
+                    return str(rknn_path)
+                else:
+                    logger.error(f"Failed to convert {model_path} to RKNN format")
+                    return None
+
+            finally:
+                release_conversion_lock(lock_file_path)
+        else:
+            logger.info(
+                f"Another process is converting {model_path}, waiting for completion..."
+            )
+
+            if wait_for_conversion_completion(rknn_path, lock_file_path):
                 return str(rknn_path)
             else:
-                logger.error(f"Failed to convert {model_path} to RKNN format")
+                logger.error(f"Timeout waiting for conversion of {model_path}")
                 return None
 
     return None