# Object Detection - Custom This function block detects objects in images using custom model files you provide. It lets you load a detector (weights, config, and class list), choose which classes to detect, and control detection sensitivity with a simple slider. The block outputs an annotated image plus structured detection data for further processing. ## 📥 Inputs `Image Any` The image to be analyzed for object detections. ## 📤 Outputs `Image Any` Annotated image with detection boxes and labels. `Object Count` Number of detected objects. `Object Locations` List of detected object center positions (multiple outputs allowed). `Object Sizes (w, h)` Width and height for each detected object (multiple outputs allowed). `Object Class` Class name for each detected object (multiple outputs allowed). `Rectangles` Bounding rectangle coordinates for each detection (multiple outputs allowed). ## 🕹️ Controls `Open Weight File` Button to choose the model weights file. `Open Config File` Button to choose the model configuration file. `Open Class File` Button to choose a text file listing class names. `Class Names` Table where available classes are listed and you can enable/disable each class. `Confidence Threshold %` Slider to set detection confidence sensitivity (higher = stricter). ## ⚙️ Running mechanism * The block requires three resources to run: a weight file, a config file, and a class list file. Load them using the three file buttons. * After the files are provided, the detector is initialized and remains ready until you change the files. * When an image is provided to the input, the block runs the detector on the image and outputs: * an annotated image with boxes/labels, * count and positions, * sizes, classes, and rectangle coordinates for each detection. * Changing weight or config files triggers reloading of the detector so new models are used for subsequent evaluations. ## ✨ Features * Load-your-own model support (weights, config, and class list). * Select which classes to detect via an easy checklist. * Adjustable confidence threshold with immediate effect. * Outputs both visual results and structured data (counts, positions, sizes, rectangles). * Works with multiple detected objects and returns results in list form for downstream blocks. ## 📝 Usage instructions 1. Click `Open Weight File` and select the model weight file. 2. Click `Open Config File` and select the model configuration file. 3. Click `Open Class File` and select the class names file. The class list will populate automatically. 4. Enable only the classes you want to detect in the `Class Names` table. 5. Adjust `Confidence Threshold %` to balance sensitivity vs false positives. 6. Provide the image to the `Image Any` input and run the scenario to get annotated image and detection data. ## 💡 Tips and Tricks * To preview results interactively, connect the `Image Any` output to the `Show Image` block. * If input images are very large and detection is slow, insert `Image Resizer` before this block to lower resolution and increase processing speed. * Limit analysis to a specific area by using `Image ROI` or `Image ROI Select` upstream so the detector focuses only on regions of interest. * For tracking detections across frames, link this block’s detection outputs to `Object_Detection_Tracker`. * If you need custom drawing or overlays beyond the built-in annotations, use `Draw Detections` with the detection rectangles and counts provided by this block. * Save interesting frames with detections using `Image Logger` or `Image Write` / `Record Video` for later review. * Monitor performance and GPU usage with `GPU Statistics` when running heavier models. (hint: enable only required classes and increase the confidence threshold to reduce false positives and speed up post-processing) ## 🛠️ Troubleshooting * Missing model files: Ensure all three files (weight, config, class list) are selected. The block cannot run without them. * No detections: Try lowering `Confidence Threshold %` or enable more classes in the class table. Also verify the class names file matches the model. * Too many false detections: Increase `Confidence Threshold %` and enable only the relevant classes to reduce noise. * Slow performance: Reduce input image size with `Image Resizer` or use smaller models; consider offloading to a GPU if available and monitor with `GPU Statistics`. * Incorrect class names or mismatched files: Verify the class file corresponds to the loaded model (class order and names must match the model training). ## 🔗 Recommended block combinations * `Show Image` — Preview annotation output. * `Image Resizer` — Speed up detection on large images. * `Image ROI` / `Image ROI Select` — Focus detection on specific areas. * `Object_Detection_Tracker` — Track detected objects over time. * `Draw Detections` — Custom visualization using detection rectangles and counts. * `Image Logger` / `Image Write` / `Record Video` — Save annotated results for audit or later analysis. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. 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