You can find all code related to this project on github. Traffic signs are an integral part of our road infrastructure. They provide critical information, sometimes compelling recommendations, for road users, which in turn requires them to adjust their driving behaviour to make sure they adhere with whatever road regulation currently enforced.
Without such useful signs, we would most likely be faced with more accidents, as drivers would not be given critical feedback on how fast they could safely go, or informed about road works, sharp turn, or school crossings ahead. In our modern age, around 1. This number would be much higher without our road signs.
Naturally, autonomous vehicles must also abide by road legislation and therefore recognize and understand traffic signs. Traditionally, standard computer vision methods were employed to detect and classify traffic signs, but these required considerable and time-consuming manual work to handcraft important features in images.
Instead, by applying deep learning to this problem, we create a model that reliably classifies traffic signs, learning to identify the most appropriate features for this problem by itself. The dataset is plit into training, test and validation sets, with the following characteristics:. Moreover, we will be using Python 3. You can see below a sample of the images from the dataset, with labels displayed above the row of corresponding images.
Some of them are quite dark so we will look to improve contrast a bit later. There is also a significant imbalance across classes in the training set, as shown in the histogram below. Some classes have less than images, while others have over This means that our model could be biased towards over-represented classes, especially when it is unsure in its predictions.
We will see later how we can mitigate this discrepancy using data augmentation. We initially apply two pre-processing steps to our images:. Grayscale We convert our 3 channel image to a single grayscale image we do the same thing in project 1 — Lane Line Detection — you can read my blog post about it HERE.
Image Normalisation We center the distribution of the image dataset by subtracting each image by the dataset mean and divide by its standard deviation. This helps our model treating images uniformly. The resulting images look as follows:. We added a few tweaks and created a modular codebase which allows us to try out different filter sizes, depth, and number of convolution layers, as well as the dimensions of fully connected layers.
We mainly tried 5x5 and 3x3 filter aka kernel sizes, and start with depth of 32 for our first convolutional layer. The network is composed of 3 convolutional layers — kernel size is 3x3, with depth doubling at next layer — using ReLU as the activation function, each followed by a 2x2 max pooling operation. The last 3 layers are fully connected, with the final layer producing 43 results the total number of possible labels computed using the SoftMax activation function.
The network is trained using mini-batch stochastic gradient descent with the Adam optimizer. We build a highly modular coding infrastructure that enables us to dynamically create our models like in the following snippets:. The ModelConfig contains information about the model such as:. The ModelExecutor is reponsible for trainingevaluatingpredictingand producing visualizations of our activation maps. To better isolate our models and make sure they do not all exist under the same Tensorflow graph, we use the following useful construct:.
This way, we create separate graphs for every model, making sure there is no mixing of our variables, placeholders etc. We actually started with a convolutional depth of 16, but obtained better results with 32 so settled on this value. We also compared color vs grayscale, standard and normalised images, and saw that grayscale tended to outperform color. Moreover, we observed some erratic loss behaviour on the validation set after a given number of epochs, which actually meant our model was overfitting on the training set and not generalising.
You can see below some of our metric graphs for different model configurations. This prevents the model from overfitting. Dropout was introduced by Geoffrey Hinton, a pioneer in the deep learning space. In the paper, the authors apply varying degrees of dropout, depending on the type of layer.
I therefore decided to adopt a similar approach, defining two levels of dropout, one for convolutional layers, the other for fully connected layers:.GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together.
If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. Road sign detection is a very critical and trending concept for autonomous vehicles.
The creation of an intelligent system to detect the road signs and convert them to speech commands can be the integral part of such autonomous vehicles in very near future. This paper presents a study to recognize traffic sign patterns using Neural Networks technique. The images are pre-processed with several image processing techniques, such as threshold techniques, Gaussian filter, Canny edge detection, Contour and Fit Ellipse.
Then, the Neural Networks stages are performed to recognize the traffic sign patterns. The first step of the preprocessing is to adjust the image size, reducing the space occupied when the resolution of the input image is too high.
If the image size were too high, it would slow down the execution of the algorithms to the point that it would restrict the program execution. The next step is to transform the input image into a gray scale image, so the edge detection algorithm can be applied. The algorithm used to detect edges in the image is the Canny Edge Detection, which takes a grayscale image as input and produces another grayscale image as output, where only the edges are shown.
Then, Morphological Operations such as dilation and erosion are applied. After this, Region of Interests or Contours are identified to locate the subject i. There are many types of traffic signs, but their shapes are very limited and characteristic, so knowing the shape allows us to determine if an ROI contains a potential traffic sign or not.
The Convolutional Neural Network CNN is a deep learning approach that stacks several convolutional, subsampling and non-linear activation layers in sequence.
Recently, the CNN has become a breakthrough technique in the field of artificial intelligence for object classification and pattern recognition applications such as handwritten digit recognition, speech recognition, object classification, and face identification.
The following graph shows to what extent classifier is able to correctly classify the traffic sign from a given set of classes of traffic sign dataset:. The graph shows to what extent validator is able to accurately predict the traffic sign from a given set of classes of traffic sign data-set:.
Python Project on Traffic Signs Recognition with 95% Accuracy using CNN & Keras
The first layers of the network C1 up to S2 function as a trainable feature extractor. The feature extraction layers C1 up to S2 have specific constraints such as local connectivity and weight sharing. With these constraints, the first layers are able to extract position invariant features from two-dimensional shapes. The classification layers n1 and n2 at the output are fully connected MLPs. These layers use the extracted local features to perform classification of the input image.
The details of the three different types of layers are described in the next subsections. BelgiumTSC Training BelgiumTSC Testing I am working on a traffic sign recognition project and have tried several different approaches with no luck. My approach is classical 2 phase: CascadeClassification is done on the entire frame for the detection of potential sign ROIs followed by a second recognition phase only on the ROIs. Status quo: I have trained a CascadeClassifier which detects signs quite well.
It is trained for all traffic signs and delivers a certain but tolerable amount of false detection which I want to rule out in the recognition phase. ROIs are square, ranging between pixels, color. I decided to do all processing on gray images only due to CPU limitations and the requirement to work at night as well. This worked somehow but performance was bad.
Main problems:. I used the Lowe criterion but the discrimination was again not good enough:. It somehow worked but again with the same problems a the other approaches. In addition a huge computational burden comes with this.
So I would exclude solution c for the future. Thanks for your suggestions. The CNN works fine, however I still have problems with the cascade classifier. No matter how I train, it always detects "round signs" much better than "triangle signs" or "filled signs". I have trained many many cascades with image counts from up tosame numbers for negatives. I tried to equalize the distribution between these sign types to avoid getting biased towards the better represented signs. Any suggestions? Asked: Haar Cascade detecting only faces no heads?
Occasional crashes in cv::CascadeClassifier. How to use CascadeClassifier with a mask. Why this picture is classified to Upperbody? First time here? Check out the FAQ! Hi there!CNN help in running neural networks directly on images and are more efficient and accurate than many of the deep neural networks. ConvNet models are easy and faster to train on images comparatively to the other models.
We will be using keras package to build CNN model. The German traffic signs detection dataset is provided here.
The dataset consists of images with 43 different classes. The images are distributed unevenly between those classes and hence the model may predict some classes more accurately than other classes. We can populate the dataset with various image modifying techniques such as rotation, colour distortion or blurring the image. We will be training the model on the original dataset and will see the accuracy of the model.
One of the limitations of the CNN model is that they cannot be trained on a different dimension of images.
So, it is mandatory to have same dimension images in the dataset. We need to compress or interpolate the images to a single dimension. Not, to compress much of the data and not to stretch the image too much we need to decide the dimension which is in between and keep the image data mostly accurate.
We will transform the image into the given dimension using opencv package. Interpolation will define what type of technique you want to use for stretching or for compressing the images.
Opencv provides 5 types of interpolation techniques based on the method they use to evaluate the pixel values of the resulting image. The dataset consist of 43 classes total. Total number of images are present in the dataset. We can plot the histogram for number of images present for different traffic signs. ClassId is the unique id given for each unique traffic signs. As, we can see from the graph that the dataset does not contain equal amount of images for each class and hence, the model may be biased in detecting some traffic signs more accurately than other.
Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. So far ellipse detection works perfect as long as picture is good quality. But as you see in pictures bellow, that red extraction does not work OK, because of poor quality of picture frames, by my opinion. So my question is is there another way of detecting traffic sign like this or extracting red areas out of it, which by the way can be very faint like in last picture?
This is converted to HSV, as you can see red areas look the same color as nearby trees. Thats how I'm suppose to know it's red but I can't. This is with red colors extracted.
Problematic sign:. You can find a review of traffic signs detection methods here and here. In my experience, I found that shape-based methods works pretty good, because the color may change a lot under different lighting conditions, camera quality, etc. Since you need to detect speed traffic signs, which I assume are always circular, you can use an ellipse detector to find all circular objects in your image, and then apply some validation to determine if it's a traffic sign or not.
Well, since you're looking for perspective distorted circles, you are in fact looking for ellipses. Real-time ellipse detection is an interesting although limited research topic. Libuda, I. Grothues, K. Kraiss, Ellipse detection in digital image data using geometric features, in: J. Braz, A. Ranchordas, H. Arajo, J. Jorge Eds. Fornaciari, A. Prati, R. Cucchiara, "A fast and effective ellipse detector for embedded vision applications", Pattern Recognition, linkcode.
I tried the method 2 without any preprocessing. You can see that at least the sign with the red border is detected very good:. I want to show you my result of basically what you did simple operations should be easily transferable to android openCV :. From your result, please mind that findContours alters the input image, so maybe you extracted the ellipse but just don't see it in the image anymore, if you saved the image AFTER findContours.
How Do I Detect Traffic Sign Using Opencv
I created a haar cascade for a traffic sign roundabout in my case and used opencv ORB to match features and remove any false positives. For image recognition used Google's tensorflow and results were spectacular. Learn more.Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
Traffic Sign Recognition TSR is used to regulate traffic signs, warn a driver, and command or prohibit certain actions. A fast real-time and robust automatic traffic sign detection and recognition can support and disburden the driver and significantly increase driving safety and comfort. Automatic recognition of traffic signs is also important for automated intelligent driving vehicle or driver assistance systems. This paper presents a study to recognize traffic sign patterns using openCV technique.
The images are extracted, detected and recognized by pre-processing with several image processing techniques, such as, threshold techniques, Gaussian filter, canny edge detection, Contour and Fit Ellipse. Then, the stages are performed to detect and recognize the traffic sign patterns.
The system is trained and validated to find the best network architecture. The experimental results show the highly accurate classifications of traffic sign patterns with complex background images and the computational cost of the proposed method.
Article :. DOI: Need Help?This tutorial is inspired by PyImageSearch readers who have emailed me asking for speed estimation computer vision solutions. Many of us live in apartment complexes or housing neighborhoods where ignorant drivers disregard safety and zoom by, going way too fast.
We feel almost powerless. When there is a speed bump, they speed up almost as if they are trying to catch some air! But what if we could catch these reckless neighborhood miscreants in action and provide video evidence of the vehiclespeedand time of day to local authorities? Once in the cloud, you can provide the shareable link to anyone you choose. I sincerely hope it will make a difference in your neighborhood.
In this tutorial, we will review the concept of VASCAR, a method that police use for measuring the speed of moving objects using distance and timestamps. By eliminating the human component, our system will rely on our knowledge of physics and our software development skills.
Our system relies on a combination of object detection and object tracking to find cars in a video stream at different waypoints. In order to utilize the VASCAR method, police must know the distance between two fixed points on the road such as signs, lines, trees, bridges, or other reference points. The speed is automatically computed as the computer already knows the distance per Equation 1.
If they press the button late first reference point and then early second reference pointthen your speed will be calculated faster than you are actually going since the time component is smaller. If you are ever issued a ticket by a police officer and it says VASCAR on it, then you have a very good chance of getting out of the ticket in a courtroom.
You can and should fight it. Be prepared with Equation 1. We will average the speed between all four points with a goal of having a better estimate of the speed. Our system is also dependent upon the distance and time components. Our config. You can tweak each configuration to your needs. We will be taking advantage of both the CentroidTracker and TrackableObject classes in this project. Our trackable object class, on the other hand, includes additional attributes that we will keep track of including timestamps, positions, and speeds.
This video is provided for demo purposes; however, take note that you should not rely on video files for accurate speeds — the FPS of the video, in addition to the speed at which frames are read from the file, will impact speed readouts.
Our pre-trained Caffe MobileNet SSD object detector used to detect vehicles files are included in the root of the project. It is identical to the live script, with the exception that it uses a prerecorded video file. Refer to this note:. For accurate speeds, you must set up the full experiment with a camera and have real cars drive by. It is one of the longer scripts we cover in Raspberry Pi for Computer Vision.
Performing detection on every frame would be too computationally expensive for the RPi. Instead, we use an object tracker to lessen the load on the Pi. Detect objects i. As mentioned previously, we have four speed estimation zones. Line 26 is the most important value in this configuration.
You will have to physically measure the "distance" on the road from one side of the frame to the other side. It will be easier if you have a helper to make the measurement. Have the helper watch the screen and tell you when you are standing at the very edge of the frame. Put the tape down on the ground at that point. Stretch the tape to the other side of the frame until your helper tells you that they see you at the very edge of the frame in the video stream.
Take note of the distance in meters — all your calculations will be dependent on this value.