Human-Object Interaction Detection with RF-DETR

Human-object interaction detection extends object detection by identifying the apparent relationship between people and nearby equipment, such as a worker operating a forklift or pushing a cart. The article presents two Roboflow Workflow approaches that avoid training a dedicated interaction model by using RF-DETR to localize warehouse workers and objects, then Gemini vision-language models to interpret their interactions. In the first, an RF-DETR Small model trained on roughly 1,200 warehouse images detects people, forklifts, pallets, carts, and fuse boxes, achieving 77.5% mAP@50, and Gemini analyzes an annotated full scene to produce cautious, review-oriented interaction summaries. The second workflow focuses on forklift safety by detecting people and forklifts, cropping expanded person regions, and asking Gemini to classify each person as safe or unsafe according to rules distinguishing seated operators from people on forklift structures, near raised loads, or in vehicle paths; it returns an annotated image, structured JSON report, and event log. The approach offers flexibility and can identify interactions outside a fixed label set, but its language-model judgments are less deterministic than dedicated classifiers, so results should be tested in target environments and reviewed by humans before operational use.

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