Tell me about a time when you had to overcome a major hurdle or obstacle in a computer vision project. How did you handle it?

In a computer vision project, we were developing an image recognition system for a retail client. The major hurdle we faced was training the model with a limited dataset. To overcome this, I collaborated with the client to gather more labeled images and used data augmentation techniques to artificially expand the dataset. Additionally, I implemented transfer learning by leveraging pre-trained models to boost performance. This approach yielded promising results, with the recognition accuracy significantly improving. The project was successfully completed within the set timeline and budget constraints.

During a computer vision project, we encountered a major obstacle while developing an object recognition system. The challenge was to accurately classify objects in real-time. We started by thoroughly analyzing the existing algorithm and identified its limitations. To address this, I proposed implementing a deep learning model using TensorFlow and utilizing transfer learning with a pre-trained network like ResNet. This allowed us to leverage the pre-trained network's knowledge and significantly improve the system's accuracy. In addition, I optimized the model by integrating GPU computing techniques through CUDA, which resulted in faster inference times. The final system achieved an accuracy of over 90% and performed reliably in real-time scenarios. Throughout the project, I maintained effective communication with the team, providing regular updates on progress and coordinating collaborative efforts to tackle challenges.

In a computer vision project, I encountered a significant hurdle while developing an autonomous driving system. The challenge was to accurately detect and track multiple objects in real-time. To address this, I led a multi-disciplinary team, including hardware engineers and data scientists, to devise a comprehensive solution. We first conducted a thorough analysis of existing object detection algorithms and identified their limitations in complex urban environments. We then proposed a novel approach combining deep learning models with LiDAR data fusion to enhance object detection accuracy and robustness. I implemented this solution using TensorFlow and C++ for optimal performance. Additionally, we integrated GPU computing techniques such as parallel processing and memory optimization, significantly reducing the inference time and enabling real-time object tracking. The system underwent rigorous testing, including simulated and real-world scenarios, achieving a detection accuracy of over 95%. This successful implementation earned recognition from the client and paved the way for further collaborations in autonomous vehicle development.