diff --git a/docker/rockchip/Dockerfile b/docker/rockchip/Dockerfile
index ca17070d0..b27e4f223 100644
--- a/docker/rockchip/Dockerfile
+++ b/docker/rockchip/Dockerfile
@@ -28,5 +28,5 @@ ADD https://github.com/MarcA711/rknn-models/releases/download/v1.5.2-rk3588/yolo
 
 RUN rm -rf /usr/lib/btbn-ffmpeg/bin/ffmpeg
 RUN rm -rf /usr/lib/btbn-ffmpeg/bin/ffprobe
-ADD --chmod=111 https://github.com/MarcA711/Rockchip-FFmpeg-Builds/releases/download/latest/ffmpeg /usr/lib/btbn-ffmpeg/bin/
-ADD --chmod=111 https://github.com/MarcA711/Rockchip-FFmpeg-Builds/releases/download/latest/ffprobe /usr/lib/btbn-ffmpeg/bin/
+ADD --chmod=111 https://github.com/MarcA711/Rockchip-FFmpeg-Builds/releases/download/6.0-1/ffmpeg /usr/lib/btbn-ffmpeg/bin/
+ADD --chmod=111 https://github.com/MarcA711/Rockchip-FFmpeg-Builds/releases/download/6.0-1/ffprobe /usr/lib/btbn-ffmpeg/bin/
diff --git a/docs/docs/configuration/object_detectors.md b/docs/docs/configuration/object_detectors.md
index 5b43960ec..8de8db192 100644
--- a/docs/docs/configuration/object_detectors.md
+++ b/docs/docs/configuration/object_detectors.md
@@ -370,7 +370,7 @@ $ cat /sys/kernel/debug/rknpu/load
 - By default the rknn detector uses the yolov8n model (`model: path: default-yolov8n`). This model comes with the image, so no further steps than those mentioned above are necessary.
 - If you want to use a more precise model, you can pass `default-yolov8s`, `default-yolov8m`, `default-yolov8l` or `default-yolov8x` as `model: path:` option.
   - If the model does not exist, it will be automatically downloaded to `/config/model_cache/rknn`.
-  - If your server has no internet connection, you can download the model from [this Github repository](https://github.com/MarcA711/rknn-models/releases/tag/latest) using another device and place it in the `config/model_cache/rknn` on your system.
+  - If your server has no internet connection, you can download the model from [this Github repository](https://github.com/MarcA711/rknn-models/releases) using another device and place it in the `config/model_cache/rknn` on your system.
 - Finally, you can also provide your own model. Note that only yolov8 models are currently supported. Moreover, you will need to convert your model to the rknn format using `rknn-toolkit2` on a x86 machine. Afterwards, you can place your `.rknn` model file in the `config/model_cache/rknn` directory on your system. Then you need to pass the path to your model using the `path` option of your `model` block like this:
 ```yaml
 model: