Automated Logo Detection and Classification System for Broadcast Content Analytics

The client currently relies on semi-manual processes to identify and assess logo exposures in broadcast images, leading to inefficiencies, potential inaccuracies, and limited scalability. The manual effort constrains their ability to serve a larger client base and expedite analysis delivery.

A mid-sized media analytics company specialized in brand exposure measurement during broadcast events, seeking to automate their logo detection and exposure analysis processes.

• Develop a fully automated logo detection system to improve accuracy and reduce analysis time.
• Implement a logo classification module to assign detected logos to specific brand categories with high precision.
• Enhance scalability to handle extensive and diverse broadcast video content across various formats.
• Reduce manual labor costs associated with brand exposure analysis.
• Deliver faster, more reliable insights to clients, enabling increased market reach.

• Automated logo detection in broadcast video frames using computer vision techniques.
• Training capability on labeled datasets comprising thousands of images to ensure high detection precision.
• A classification module employing neural network models to identify brand identities with at least 90% accuracy.
• Integration of diverse data sources, including public datasets, to improve model robustness and adaptability.
• An analytics dashboard for reporting detection and classification results in real-time or batch modes.

• Video data ingestion systems
• External datasets for model training
• Reporting and analytics dashboards

• Detection precision with F1 score above 88% and mAP 50 above 93.2%.
• Classification accuracy exceeding 90% in brand categorization.
• Scalable architecture to handle large volumes of broadcast video content across various formats.
• Real-time or near-real-time processing capabilities.
• Secure handling of multimedia data to ensure privacy and compliance.

The deployment of the AI-driven logo detection and classification system is expected to significantly improve operational efficiency by reducing analysis time and manual effort. This automation will enhance the accuracy of brand exposure assessments, leading to more reliable sponsorship valuation data, and enable the client to serve a broader client base while reducing costs associated with manual analysis efforts.