{"id":17127,"date":"2025-01-29T11:19:53","date_gmt":"2025-01-29T09:19:53","guid":{"rendered":"https:\/\/spatialworld.fi\/?page_id=17127"},"modified":"2025-02-05T17:11:04","modified_gmt":"2025-02-05T15:11:04","slug":"17076-2-2-2-3","status":"publish","type":"page","link":"https:\/\/spatialworld.fi\/en\/17076-2-2-2-3\/","title":{"rendered":"RaspberryPi"},"content":{"rendered":"[vc_row type=”in_container” full_screen_row_position=”middle” column_margin=”default” column_direction=”default” column_direction_tablet=”default” column_direction_phone=”default” scene_position=”center” text_color=”dark” text_align=”left” row_border_radius=”none” row_border_radius_applies=”bg” overflow=”visible” overlay_strength=”0.3″ gradient_direction=”left_to_right” shape_divider_position=”bottom” bg_image_animation=”none”][vc_column column_padding=”no-extra-padding” column_padding_tablet=”inherit” column_padding_phone=”inherit” column_padding_position=”all” column_element_direction_desktop=”default” column_element_spacing=”default” desktop_text_alignment=”default” tablet_text_alignment=”default” phone_text_alignment=”default” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_backdrop_filter=”none” column_shadow=”none” column_border_radius=”none” column_link_target=”_self” column_position=”default” gradient_direction=”left_to_right” overlay_strength=”0.3″ width=”1\/1″ tablet_width_inherit=”default” animation_type=”default” bg_image_animation=”none” border_type=”simple” column_border_width=”none” column_border_style=”solid”][vc_column_text css=”” text_direction=”default”]\n
\n

🔧 Success and Lessons Learned: Edge Computing with Raspberry Pi 5 and FME Remote Engine! 🍓💻🦎<\/span><\/h1>\n<\/header>\n[\/vc_column_text][\/vc_column][\/vc_row][vc_row type=”in_container” full_screen_row_position=”middle” column_margin=”default” column_direction=”default” column_direction_tablet=”default” column_direction_phone=”default” scene_position=”center” text_color=”dark” text_align=”left” row_border_radius=”none” row_border_radius_applies=”bg” overflow=”visible” overlay_strength=”0.3″ gradient_direction=”left_to_right” shape_divider_position=”bottom” bg_image_animation=”none”][vc_column column_padding=”no-extra-padding” column_padding_tablet=”inherit” column_padding_phone=”inherit” column_padding_position=”all” column_element_direction_desktop=”default” column_element_spacing=”default” desktop_text_alignment=”default” tablet_text_alignment=”default” phone_text_alignment=”default” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_backdrop_filter=”none” column_shadow=”none” column_border_radius=”none” column_link_target=”_self” column_position=”default” gradient_direction=”left_to_right” overlay_strength=”0.3″ width=”1\/1″ tablet_width_inherit=”default” animation_type=”default” bg_image_animation=”none” border_type=”simple” column_border_width=”none” column_border_style=”solid”][image_with_animation image_url=”17201″ image_size=”medium_large” animation_type=”entrance” animation=”None” animation_movement_type=”transform_y” hover_animation=”none” alignment=”center” border_radius=”none” box_shadow=”none” image_loading=”default” max_width=”75%” max_width_mobile=”default”][\/vc_column][\/vc_row][vc_row type=”in_container” full_screen_row_position=”middle” column_margin=”default” column_direction=”default” column_direction_tablet=”default” column_direction_phone=”default” scene_position=”center” text_color=”dark” text_align=”left” row_border_radius=”none” row_border_radius_applies=”bg” overflow=”visible” overlay_strength=”0.3″ gradient_direction=”left_to_right” shape_divider_position=”bottom” bg_image_animation=”none”][vc_column column_padding=”no-extra-padding” column_padding_tablet=”inherit” column_padding_phone=”inherit” column_padding_position=”all” column_element_direction_desktop=”default” column_element_spacing=”default” desktop_text_alignment=”default” tablet_text_alignment=”default” phone_text_alignment=”default” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_backdrop_filter=”none” column_shadow=”none” column_border_radius=”none” column_link_target=”_self” column_position=”default” gradient_direction=”left_to_right” overlay_strength=”0.3″ width=”1\/1″ tablet_width_inherit=”default” animation_type=”default” bg_image_animation=”none” border_type=”simple” column_border_width=”none” column_border_style=”solid”][vc_column_text css=”” text_direction=”default”]\n

We’re thrilled to announce that our edge computing solution using the Raspberry Pi 5<\/strong> and FME\u2019s Remote Engine<\/strong> is officially up and running<\/strong>! 🎉<\/p>\n

\n

Our Process:<\/strong><\/p>\n

    \n
  1. Frame Capture from Video Streams:<\/strong> We use the Raspberry Pi to extract frames from live video feeds in real-time, ready for immediate processing.<\/li>\n
  2. Image Preparation with FME Remote Engine:<\/strong> FME Engine processes images locally on the Pi, optimizing them for subsequent analysis.<\/li>\n
  3. Object Detection using YOLO via Darknet:<\/strong> Advanced AI models (YOLO) integrated through Darknet identify objects in the images, ensuring accurate detection of key elements.<\/li>\n
  4. Output Refinement with FME Remote Engine:<\/strong> FME further refines and filters detection results, improving the accuracy and relevancy of the data.<\/li>\n
  5. Conditional Reporting to FME Flow:<\/strong> Comprehensive reports are sent to the main FME Flow instance only when specific objects are detected with a defined probability, reducing unnecessary data transmission.<\/li>\n<\/ol>\n
    \n
    \n
    \n
    <\/div>\n<\/div>\n<\/figure>\n<\/div>\n[\/vc_column_text][image_with_animation image_url=”17200″ image_size=”full” animation_type=”entrance” animation=”None” animation_movement_type=”transform_y” hover_animation=”none” alignment=”center” border_radius=”none” box_shadow=”none” image_loading=”default” max_width=”100%” max_width_mobile=”default”][\/vc_column][\/vc_row][vc_row type=”in_container” full_screen_row_position=”middle” column_margin=”default” column_direction=”default” column_direction_tablet=”default” column_direction_phone=”default” scene_position=”center” text_color=”dark” text_align=”left” row_border_radius=”none” row_border_radius_applies=”bg” overflow=”visible” overlay_strength=”0.3″ gradient_direction=”left_to_right” shape_divider_position=”bottom” bg_image_animation=”none”][vc_column column_padding=”no-extra-padding” column_padding_tablet=”inherit” column_padding_phone=”inherit” column_padding_position=”all” column_element_direction_desktop=”default” column_element_spacing=”default” desktop_text_alignment=”default” tablet_text_alignment=”default” phone_text_alignment=”default” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_backdrop_filter=”none” column_shadow=”none” column_border_radius=”none” column_link_target=”_self” column_position=”default” gradient_direction=”left_to_right” overlay_strength=”0.3″ width=”1\/1″ tablet_width_inherit=”default” animation_type=”default” bg_image_animation=”none” border_type=”simple” column_border_width=”none” column_border_style=”solid”][vc_column_text css=”” text_direction=”default”]\n

    But the journey wasn’t without its challenges. While the Raspberry Pi 5 hardware is impressive, we faced unexpected difficulties with operating systems.<\/p>\n

    💡 What We Learned:<\/strong><\/p>\n

    ✅ Edge Workflow Success:<\/strong> The Pi’s enhanced performance, combined with FME\u2019s powerful data handling, truly shines in edge computing scenarios.<\/p>\n

    ⚠️ Challenges Faced:<\/strong><\/p>\n

      \n
    • Raspberry Pi OS:<\/strong><\/li>\n<\/ul>\n
        \n
      1. Compatibility Issues:<\/strong> Some dependencies and libraries required by FME weren’t readily available or compatible.<\/li>\n
      2. Installation Hurdles:<\/strong> Setting up FME Engine on Raspberry Pi OS was more complex than anticipated.<\/li>\n<\/ol>\n
          \n
        • Ubuntu 24.04 ARM:<\/strong><\/li>\n<\/ul>\n
            \n
          1. Camera Compatibility:<\/strong> The Raspberry Pi camera wasn’t easily compatible due to driver differences.<\/li>\n<\/ol>\n
            \n

            ✨ Our Solution:<\/strong><\/p>\n

            We transitioned to Ubuntu 24.04 ARM<\/strong> and adapted our video input by using a YouTube stream<\/strong>.<\/p>\n

              \n
            • Seamless Integration:<\/strong> Ubuntu’s compatibility with FME allowed for smoother installation and operation.<\/li>\n
            • Improved Support:<\/strong> Extensive documentation and community support for Ubuntu on ARM helped us overcome obstacles quickly.<\/li>\n<\/ul>\n
              \n

              🌟 Benefits of Our Approach:<\/strong><\/p>\n

                \n
              • Real-Time Data Processing:<\/strong> By performing computations directly on the Raspberry Pi, we achieve immediate insights without delays.<\/li>\n
              • Bandwidth Optimization:<\/strong> Local processing reduces the need to transmit large amounts of data over the network, saving bandwidth.<\/li>\n
              • Cost-Effective Scalability:<\/strong> Utilizing affordable hardware like the Raspberry Pi 5 makes it easy to scale up edge computing nodes as needed.<\/li>\n
              • Enhanced Flexibility:<\/strong> FME\u2019s Remote Engine handles diverse data formats and workflows, allowing us to adapt quickly to different scenarios.<\/li>\n
              • Improved Privacy and Security:<\/strong> Processing data at the edge minimizes the exposure of sensitive information over networks.<\/li>\n
              • Simplified Integration of Sensors:<\/strong> The ability to easily add peripherals like USB cameras, GPS modules, and temperature sensors expands the scope of data collection.<\/li>\n<\/ul>\n
                \n

                🚀 Moving Forward:<\/strong><\/p>\n

                  \n
                • Testing with USB Cameras:<\/strong> Planning to integrate USB cameras, which have more universally supported drivers in Ubuntu.<\/li>\n
                • Exploring Additional Sensors:<\/strong> Expanding our setup to include GPS, temperature, and more for comprehensive data collection.<\/li>\n<\/ul>\n
                  \n

                  🔑 Key Takeaway:<\/strong> The operating system plays a crucial role in the success of edge computing projects. If you’re venturing into similar territory, consider using Ubuntu ARM on your Raspberry Pi for a smoother experience.<\/p>\n[\/vc_column_text][\/vc_column][\/vc_row]","protected":false},"excerpt":{"rendered":"

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