Logo and Sponsorship Detection AI

Add a measurement layer to every broadcast, stream, and social post with vision AI for logo and sponsorship detection. Built for the rights holders and brands where the sponsorship deck promises exposure and the post-season report is a sample of three games coded by hand, the jersey patch shows up in 40,000 fan photos nobody counted, and the competitor's logo appeared on the LED board during the highlight that went viral. Roboflow finds every logo in every frame of broadcast, streaming, and social video and imagery, measures how long, how large, how clear, and where, and turns exposure into a defensible number with the frames behind it.

Logo Detection and Recognition:

• Detect and recognize sponsor logos on jerseys, kits, and helmets, LED and static boards, field and court markings, backdrops and press walls, and vehicles and equipment across every frame of video
• Recognize logos at any size, angle, partial occlusion, motion blur, and lighting, including the half-visible patch behind a defender and the board seen from the far camera
• Detect unauthorized, competitor, and ambush logos in venues, on apparel, and in user-generated content, so a clean-venue clause or category exclusivity is enforceable

Exposure Measurement and Valuation:

• Measure duration on screen, size as a share of frame, position, clarity, and clutter for each logo appearance, so exposure is scored by quality rather than counted by occurrence
• Attribute exposure by asset, broadcast, segment, and moment, so the jersey patch, the LED board, and the interview backdrop each carry their own number
• Feed exposure scores into media-equivalent and sponsorship valuation models with the frames behind every figure, so a renewal conversation has evidence rather than a sample

Social, Streaming, and Systems Integration:

• Monitor social platforms, fan photos, and creator content for sponsor logos, so earned exposure outside the broadcast is counted and unauthorized use is found
• Process live streams and archived broadcast at scale, so every game, race, and event is measured rather than a sample of three
• Push detections, exposure metrics, and frame evidence into sponsorship analytics, CRM, and reporting tools through API integration, with a full record behind every report

Bring intelligence to every placement today.

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More About Logo and Sponsorship Detection

What is logo detection with Vision AI?

Logo detection with vision AI uses deep-learning models to find and recognize brand logos in video and images, in broadcast and streaming footage, social media content, fan photos, and venue and event imagery, and to measure each appearance: duration on screen, size as a share of frame, position, clarity, occlusion, and clutter. For sponsorship measurement, those measurements are attributed to the asset that carried the logo, the jersey patch, the LED board, the field marking, the backdrop, and rolled into exposure and valuation metrics with the frames behind every number. The same models detect unauthorized and competitor logos for clean-venue enforcement, category exclusivity, and brand protection. Models are trained on the specific logos and placements in a sponsorship portfolio, so a new sponsor or a redesigned mark is a training update rather than a new system.

Can it integrate with our sponsorship analytics and reporting tools?

Yes. Roboflow Inference exposes a standard API and webhooks, so logo detections, exposure metrics, and frame evidence flow into your existing systems: sponsorship and media valuation platforms, CRM and partnership management systems like Salesforce and KORE, social listening and monitoring tools, media asset management and broadcast archives, and BI and reporting tools like Power BI, Tableau, and Looker, through REST, webhooks, and direct database writes. Each detection carries the sponsor, the asset and placement, the broadcast or platform, the segment and timestamp, the duration, size, position, and clarity scores, and the frame, so a sponsorship report, a renewal deck, or an exclusivity dispute is built from evidence rather than a sample.

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