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Improving Urban Traffic Monitoring with Aerial Image Annotation Services

Learn how SunTec India helped a US-based government agency improve its traffic analysis modelu2019s accuracy by 35% with high-quality aerial image annotation services, using bounding box technique and a multi-stage QA process. Visit: https://www.suntecindia.com/

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Improving Urban Traffic Monitoring with Aerial Image Annotation Services

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  1. Improving Urban Traffic Monitoring with Aerial Image Annotation Services Discover How SunTec India's Aerial Image Annotation Services Increased Object Detection Accuracy by 35% in a Traffic Analysis Model

  2. About the Client A U.S.-based government agency approached SunTec India for assistance in improving its AI-based traffic analysis model. The agency oversees urban development in a rapidly growing metropolitan region. Their traffic analysis model relies on aerial imagery to monitor and coordinate city traffic and related infrastructure operations.

  3. Project Requirements Image annotation services for 2000+ aerial photos 1 Identification of eight distinct object classes (cars, buses, pedestrians, etc.) 2 Application of the bounding box technique using the LabelImg tool 3

  4. Project Challenges Navigating low-resolution or unclear aerial images while ensuring precise annotation 1 Adapting to inconsistent lighting conditions across data samples to maintain accuracy 2 Distinguishing objects in high- density, congested traffic zones without compromising annotation quality 3

  5. Our Solution We deployed a team of five skilled annotators proficient in bounding box annotation technique. The team was responsible for: Annotation Guideline Creation: We developed comprehensive labeling criteria with visual references and a decision framework for ambiguous cases. 1 High-Precision Zooming: We instructed annotators to work at 100% zoom in for accurate bounding box placement, and then zoom out to verify context. 2

  6. Our Solution Quality Assurance: We implemented a multi- level QA process, including peer review, senior annotator checks for complex cases, and a final review by the project lead. 3 Team Collaboration: We held regular team meetings to discuss challenging cases and ensure that consistent annotation standards were followed throughout. 4 Feedback-Driven Workflow: The project manager maintained ongoing communication with the client, enabling real-time implementation of feedback and optimizing the labeling workflow throughout the project lifecycle. 5

  7. Project Outcomes Increased the AI model’s object detection accuracy by 35% through precise image labeling, resulting in more reliable and consistent traffic analysis outcomes 1 Improved real-time traffic flow monitoring by 20%, allowing faster and more informed congestion management decisions. 2 Set annotation quality standards, adopted for the agency’s future projects 3

  8. Need Help Labeling Complex Image Datasets? Get in touch with us at info@suntecindia.com to learn more about our aerial image annotation services. Read the full case study here. Website: https://www.suntecindia.com Phone: +15852830055 & +442035142601

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