With the rapid development of artificial intelligence, vision algorithms based on deep learning have played an important role in the field of road transportation. This article will explore how to use deep learning technology to detect and identify road traffic lights, and demonstrate the depth of the technology through multiple code examples.
Road traffic lights are important signals that instruct traffic participants to go and stop. Accurately detecting and identifying traffic lights is critical to the development of intelligent transportation systems and autonomous driving technology. Traditional computer vision algorithms have some limitations in the detection and recognition of traffic lights, while methods based on deep learning have better performance and robustness.
Data set preparation:
Deep learning algorithms usually require large amounts of labeled data for training. For the detection and recognition of road traffic lights, images with different lighting conditions, weather conditions and angles need to be collected. Training and test sets can be constructed by taking images on actual roads or using existing public datasets.
Design of Convolutional Neural Network (CNN):
Convolutional neural networks are a commonly used architecture in deep learning and are particularly suitable for image processing tasks. In order to realize the detection and recognition of traffic lights, a CNN model can be designed. The model usually includes convolutional layers, pooling layers, and fully connected layers to extract image features and classify them.
Here is a simplified CNN model example:
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(64, 64, 3)))
model.add(MaxPooling2D((