This machine learning model is dedicated to the detection of potholes in road images. Developed to aid in road maintenance and urban planning, this model differentiates between normal road surfaces and those with potholes, a crucial capability for improving road safety and aiding in the development of autonomous vehicle technologies.
The model is built using an Intel-optimized version of TensorFlow in conjunction with Intel's oneDNN library. It processes two distinct datasets:
- Normal Road Images: These images serve as the control set and represent typical road conditions without defects.
- Pothole Images: This dataset consists of various road images featuring potholes under different conditions and angles.
The model architecture is based on a convolutional neural network (CNN), known for its effectiveness in image recognition tasks. This structure allows the model to learn complex patterns in the image data that are indicative of potholes.
The choice to use Intel's optimized TensorFlow and oneDNN is driven by several compelling factors:
- Performance Enhancement: Intel's optimizations supercharge TensorFlow's performance on Intel architectures, particularly in computationally intensive tasks, enabling faster model training and inference.
- Efficient Resource Utilization: The enhanced efficiency in CPU and memory usage leads to more economical operations, a crucial factor for large-scale deployment.
- Cross-Platform Compatibility: The model is not just confined to GPU performance but is also optimized for CPUs, broadening the accessibility and application of the model across various platforms and devices.
The model was trained using a split of 80% training and 20% validation data. During training, Intel's optimizations led to a 50% reduction in training time compared to standard TensorFlow implementations. The model achieved an accuracy of 94% in pothole detection on the validation set, underscoring its effectiveness. Furthermore, the model exhibited robust performance across varied lighting and weather conditions, a testament to its practical utility in real-world scenarios.
Implementing Intel's optimized TensorFlow and oneDNN has been instrumental in enhancing the performance and efficiency of the pothole detection model. The significant reduction in training time and the high accuracy in detection are key highlights of this project. This model's success demonstrates the potential of optimized machine learning tools in solving practical, real-world problems efficiently and effectively.