WiMi Hologram Cloud Inc., a hologram augmented reality technology provider, has developed an innovative image classification system tailored to the automotive sector. This system integrates deep learning and machine learning models to enhance the accuracy and efficiency of image classification in various applications.
WiMi uses a convolutional neural network (CNN) as the deep learning model, extracting local features through multiple convolutional and pooling layers, and employing a fully connected layer to obtain a high-level representation of the image. The machine learning model, a support vector machine (SVM), is then used for classification based on these high-level features.
SVM is a classical machine learning algorithm that can classify feature vectors based on their linear divisibility. By fusing deep learning and machine learning models, the feature extraction ability of the deep learning model and the classification ability of the machine learning model are fully utilized to improve the accuracy of image classification. Due to the separation of the deep learning model and the machine learning model, either module can be flexibly adapted and replaced for different image classification tasks and data sets to achieve more accurate and efficient image classification tasks.
The synergy between deep learning and machine learning is pivotal in enhancing image classification accuracy. By combining the feature extraction process of deep learning with the classification capabilities of machine learning, WiMi optimizes the computational process, improving efficiency. The modular separation of the deep learning and machine learning models allows for flexible adaptation and replacement, catering to diverse image classification tasks and data sets.
For intelligent transportation, the system can in real time detect and identify vehicle types, license plate numbers and traffic signs, providing crucial information for traffic management. In autonomous driving, the system contributes to real-time detection and recognition of lane lines, traffic signs and obstacles, enabling precise control and safe driving of autonomous vehicles.
Moving forward, WiMi plans to enhance the image classification system by expanding data sets, optimizing network structures, introducing attention mechanisms, combining multiple models and implementing transfer learning. This continuous optimization aims to improve system performance and extend applications across various industries.