diff --git a/frigate/detectors/rockchip.py b/frigate/detectors/rockchip.py
new file mode 100644
index 000000000..295aa5590
--- /dev/null
+++ b/frigate/detectors/rockchip.py
@@ -0,0 +1,193 @@
+"""Handles detection with rockchip TPUs."""
+
+import datetime
+import logging
+import multiprocessing as mp
+import os
+import queue
+import signal
+import threading
+from abc import ABC, abstractmethod
+
+import numpy as np
+from setproctitle import setproctitle
+from rknnlite.api import RKNNLite
+
+from frigate.detectors.detector import ObjectDetector
+from frigate.util import EventsPerSecond, SharedMemoryFrameManager, listen, load_labels
+
+logger = logging.getLogger(__name__)
+
+class LocalObjectDetector(ObjectDetector):
+    def __init__(self, rk_device=None, model_path=None, labels=None):
+        self.fps = EventsPerSecond()
+        if labels is None:
+            self.labels = {}
+        else:
+            self.labels = load_labels(labels)
+
+        try:
+            self.rknn = RKNNLite()
+            self.rknn.load_rknn(model_path or "/precompiled_models/rockchip_rk1808_default.rknn")
+        except ValueError:
+            logger.error(f"Not able to load rk model at {model_path}.")
+            raise
+
+        try:
+            self.rknn.init_runtime('rk1808')
+        except ValueError as e:
+            logger.error(f"Not able to init rk model: {e}.")
+            raise
+
+    def detect(self, tensor_input, threshold=0.4):
+        detections = []
+
+        raw_detections = self.detect_raw(tensor_input)
+
+        for d in raw_detections:
+            if d[1] < threshold:
+                break
+            detections.append(
+                (self.labels[int(d[0])], float(d[1]), (d[2], d[3], d[4], d[5]))
+            )
+        self.fps.update()
+        return detections
+
+    def detect_raw(self, tensor_input):
+        self.rknn.inference(inputs=[tensor_input])
+        # TODO figure out how to use the model output to get values
+        self.interpreter.set_tensor(self.tensor_input_details[0]["index"], tensor_input)
+        self.interpreter.invoke()
+
+        boxes = self.interpreter.tensor(self.tensor_output_details[0]["index"])()[0]
+        class_ids = self.interpreter.tensor(self.tensor_output_details[1]["index"])()[0]
+        scores = self.interpreter.tensor(self.tensor_output_details[2]["index"])()[0]
+        count = int(
+            self.interpreter.tensor(self.tensor_output_details[3]["index"])()[0]
+        )
+
+        detections = np.zeros((20, 6), np.float32)
+
+        for i in range(count):
+            if scores[i] < 0.4 or i == 20:
+                break
+            detections[i] = [
+                class_ids[i],
+                float(scores[i]),
+                boxes[i][0],
+                boxes[i][1],
+                boxes[i][2],
+                boxes[i][3],
+            ]
+
+        return detections
+
+def run_detector(
+    name: str,
+    detection_queue: mp.Queue,
+    out_events: dict[str, mp.Event],
+    avg_speed,
+    start,
+    model_path,
+    model_shape,
+    rk_device,
+):
+    threading.current_thread().name = f"detector:{name}"
+    logger = logging.getLogger(f"detector.{name}")
+    logger.info(f"Starting detection process: {os.getpid()}")
+    setproctitle(f"frigate.detector.{name}")
+    listen()
+
+    stop_event = mp.Event()
+
+    def receiveSignal(signalNumber, frame):
+        stop_event.set()
+
+    signal.signal(signal.SIGTERM, receiveSignal)
+    signal.signal(signal.SIGINT, receiveSignal)
+
+
+    frame_manager = SharedMemoryFrameManager()
+    object_detector = LocalObjectDetector(
+        rk_device=rk_device,
+        model_path=model_path,
+    )
+
+    outputs = {}
+    for name in out_events.keys():
+        out_shm = mp.shared_memory.SharedMemory(name=f"out-{name}", create=False)
+        out_np = np.ndarray((20, 6), dtype=np.float32, buffer=out_shm.buf)
+        outputs[name] = {"shm": out_shm, "np": out_np}
+
+    while not stop_event.is_set():
+        try:
+            connection_id = detection_queue.get(timeout=5)
+        except queue.Empty:
+            continue
+        input_frame = frame_manager.get(
+            connection_id, (1, model_shape[0], model_shape[1], 3)
+        )
+
+        if input_frame is None:
+            continue
+
+        # detect and send the output
+        start.value = datetime.datetime.now().timestamp()
+        detections = object_detector.detect_raw(input_frame)
+        duration = datetime.datetime.now().timestamp() - start.value
+        outputs[connection_id]["np"][:] = detections[:]
+        out_events[connection_id].set()
+        start.value = 0.0
+
+        avg_speed.value = (avg_speed.value * 9 + duration) / 10
+
+class RockchipTPUProcess:
+    def __init__(
+        self,
+        name,
+        detection_queue,
+        out_events,
+        model_path,
+        model_shape,
+        rk_device=None,
+    ):
+        self.name = name
+        self.out_events = out_events
+        self.detection_queue = detection_queue
+        self.avg_inference_speed = mp.Value("d", 0.01)
+        self.detection_start = mp.Value("d", 0.0)
+        self.detect_process = None
+        self.model_path = model_path
+        self.model_shape = model_shape
+        self.rk_device = rk_device
+        self.start_or_restart()
+
+    def stop(self):
+        self.detect_process.terminate()
+        logging.info("Waiting for detection process to exit gracefully...")
+        self.detect_process.join(timeout=30)
+        if self.detect_process.exitcode is None:
+            logging.info("Detection process didnt exit. Force killing...")
+            self.detect_process.kill()
+            self.detect_process.join()
+
+    def start_or_restart(self):
+        self.detection_start.value = 0.0
+        if (not self.detect_process is None) and self.detect_process.is_alive():
+            self.stop()
+        self.detect_process = mp.Process(
+            target=run_detector,
+            name=f"detector:{self.name}",
+            args=(
+                self.name,
+                self.detection_queue,
+                self.out_events,
+                self.avg_inference_speed,
+                self.detection_start,
+                self.model_path,
+                self.model_shape,
+                self.rk_device,
+            ),
+        )
+        self.detect_process.daemon = True
+        self.detect_process.start()
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