Real-time multi-object detection for autonomous vehicles.

Artificial Intelligence, Machine Learning & IoT

This project focuses on developing a real-time multi-object detection and classification system for autonomous (self-driving) vehicles. The system identifies various road objects from live video streams and supplies this information to the vehicle's decision-making system, enabling it to respond appropriately while driving. The solution is designed to process real-world road conditions efficiently, combining object detection with lane detection to support safe autonomous navigation.

Business requirements & challenges

Business requirements & challenges

The project required a highly accurate, real-time computer-vision system capable of identifying multiple types of objects on the road under real-world driving conditions.

Key requirements

• Detect road objects — vehicles, pedestrians, strollers, traffic lights, traffic signs and more
• Classify each detected object accurately
• Send classified object data to the system responsible for controlling the self-driving vehicle
• Operate effectively under real-world driving conditions
• Perform object detection in real time with minimal latency

Major challenges

• Maintaining high detection accuracy in real-world scenarios
• Supporting real-time processing while multiple objects appear simultaneously
• Training deep-learning models across hundreds of thousands of iterations and fine-tuning them for accuracy

Solution

Solution

An AI-based solution built on deep-learning object detection, integrated with lane detection and wired directly into the vehicle's control system.

• Trained deep-learning models to detect and classify different road objects
• YOLO v2 (You Only Look Once) architecture with Darknet-19 as the backbone network
• Custom Network-in-Network (NiN) architecture for object detection and classification
• Single-pass frame scanning — YOLO scans each frame only once for fast multi-object detection
• Integrated a lane-detection algorithm alongside object detection
• Processed output sent directly to the self-driving vehicle system to determine the appropriate driving action

Stack

Technologies used

How it works

Process workflow

1. 01Real-time object identification
2. 02Human and animal identification
3. 03Vehicle identification
4. 04Track / lane identification
5. 05Real-time object processing and categorization
6. 06Real-time processing with a response mechanism to manage the car
7. 07Automated car driving

Business benefits

Business benefits

Detected objects are supplied to the self-driving vehicle's control system, so the vehicle can automatically perform the appropriate action. The algorithm is one of the core technologies required for autonomous driving.

• Detected objects supplied to the self-driving vehicle control system
• Vehicle automatically performs appropriate actions based on the detected objects
• Serves as one of the core technologies in the autonomous-driving stack
• Enables key self-driving functionality — foundational business value

At a glance

Key highlights