Advanced AI-powered Object Detection System for Enhanced Vehicle Safety in Automotive ADAS

The client faces difficulties in accurately detecting unknown objects in real-time within complex driving environments, which adversely affects vehicle safety and driver assistance capabilities. Existing systems lack precision and adaptability for diverse road scenarios, necessitating an advanced AI-powered solution to improve object recognition accuracy and real-time responsiveness.

A leading automotive manufacturer seeking to augment their Advanced Driver Assistance Systems (ADAS) with AI-driven object detection and real-time alerts to improve vehicle safety and driver confidence.

• Develop a highly accurate AI-based object detection system with an accuracy rate exceeding 98.5%.
• Process and annotate over 1.5 million objects across 20+ categories related to the driving environment, including vehicles, pedestrians, road markings, and background.
• Implement a scalable infrastructure enabling real-time object detection and alerts to improve driver safety.
• Automate data annotation and model training workflows with custom scripting and MLOps practices to enhance efficiency.
• Establish a robust data pipeline supporting continuous model updates and scalability for future ADAS enhancements.

• Support manual and automated data annotation with detailed labeling techniques such as polygons, polylines, and segmentation for complex objects.
• Develop custom scripts to automate data processing, annotation validation, and model training workflows.
• Integrate annotated datasets into deep neural network models built with frameworks like PyTorch and TensorFlow.
• Implement continuous model training and deployment pipelines aligned with MLOps best practices.
• Design a real-time image processing pipeline that captures, analyzes, and updates object detection models based on incoming data streams.
• Enable camera association labeling and qualitative analysis through remote and local team collaboration workflows.

• Data collection systems for input image streams
• Model deployment platforms for real-time updates
• Quality analysis tools for ongoing assessment of detection performance

• Achieve annotation accuracy of ≥98.5%
• Support processing of over 21,000 images and 1.5 million objects
• Ensure system scalability to incorporate additional object categories and models in the future
• Maintain robust data security and privacy standards in cloud infrastructure
• Optimize for minimal latency to enable real-time alerts with high reliability

The implementation of this advanced object detection system is expected to significantly improve vehicle safety by achieving over 98.5% detection accuracy, enabling real-time alerts for drivers, and supporting the integration of more reliable ADAS functionalities. This will enhance driver confidence, reduce accident risks, and lay the groundwork for scalable future AI automotive innovations.