Analyzing Truck Load Using Image Recognition

content/posts/analyzing-truck-load-using-image-recognition/index.md

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+title = "Analyzing Truck Load Using Image Recognition"
+date = 2026-02-12
+
+[taxonomies]
+categories = ["Software"]
+
+[extra]
+author = "Emil Miler"
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+
+A friend of mine asked for help expanding an image recognition system he is implementing at work. The [Roboflow](https://roboflow.com/) pipeline analyzes images of open trucks and tags all visible cargo with the appropriate label and dimensions.
+
+The goal was to extend the output to display the percentage of the truck’s maximum capacity being used. To achieve this, I integrated a custom Python script into the existing pipeline.
+
+<!-- more -->
+
+Roboflow is a platform for managing image datasets, training computer vision models, and running image recognition workflows. My friend had already trained a functioning model before I got involved.
+
+![Image Output](image-output.png)
+
+Since the system operates on 2D images, we can calculate only the rough two-dimensional surface coverage of the truck, not the full cargo volume. For their specific use case, however, this level of estimation is sufficient.
+
+I extended the existing pipeline by adding a new branch with a custom script block called _Total Coverage_. This block allows for custom logic and can interact directly with the [Roboflow API](https://inference.roboflow.com/workflows/about/) to access workflow data and inference results.
+
+![Roboflow Pipeline](roboflow-pipeline.png)
+
+Below is the Python script I implemented to extract the inference data and calculate both the total surface coverage and the number of detected objects.
+
+```python
+def run(self, model_output) -> dict:
+    object_count = len(model_output)
+    truck_area = 0
+    cargo_area = 0
+
+    for xyxy, mask, confidence, class_id, tracker_id, data in model_output:
+        w = data.get("width", 0)
+        h = data.get("height", 0)
+        label = data.get("class_name", "").lower()
+        area = w * h
+
+        if label == "truck":
+            truck_area = area
+            object_count -= 1
+        else:
+            cargo_area += area
+
+    coverage = round((cargo_area / truck_area * 100), 1) if truck_area > 0 else 0
+
+    return {
+        "total_coverage": coverage,
+        "object_count": object_count,
+    }
+```
+
+The tricky part was handling the `model_output` correctly, otherwise the logic is straightforward. Needless to say, this remains an estimation rather than a precise calculation. Here is an example of the script output in JSON format.
+
+```json
+"truck_stats": {
+    "total_coverage": 51.8,
+    "object_count": 8
+},
+```
+
+This was an interesting exercise, as it was my first time working with Roboflow and contributing to an image recognition project that is actively used in production.