OpenCV for Autonomous Vehicle

Computer Vision for Autonomous Driving

Vision is the main source of information in self-driving vehicles. For it to steer by itself, it needs to ‘see’, understand, and sense the surrounding world just as a human would. For example, navigate within lanes, avoid crashing into other cars/pedestrians, stop at stop signs, or traffic lights when red. Advanced computer vision, Artificial Intelligence, and sensor technology have made it possible for us to envision the possibility of driverless cars. The machine can sense using multiple sensors and image reading hardware(cameras), based on the inputs received, through deep learning algorithms, the machine can locate itself, make decisions in real-time about how to navigate in the perceived world. The key underlying technology behind self-driving cars is an integration of many technologies including Automation, Artificial Intelligence, Computer Vision. The challenge is resilience, how can we make machines more durable, safer, reduce collisions, instill pedestrian-first methodology, and succeed in realizing our vision of autonomous transportation is not so far away in future? AV is not just a device but a system, a collection of innovations applied in a well-architected way.

Figure 2 An Autonomous Vehicle Components (Source: Google Driverless Car Article base)

To build an intelligent vehicle, we need to enable it with tools, complex algorithms, machine learning systems, and a powerful computing system to capture visual data and execute output in real-time. The sensory hardware such as cameras and sensors can gather large sets of data around our location, traffic, weather conditions, road conditions, crowds, objects, and others. The data is what assists self-driving through awareness and makes vital decisions swiftly. The large scale of data sets can be further used in training deep learning algorithms needed to make a machine capable of being autonomous with resilience. To successfully build and test an intelligent AV we need to understand its basic architecture. Click here to learn Data Science Course

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The first of these four categories for the technology behind self-driving automobiles is perception. The machine can comprehend the world while it is being assembled. identify vehicles, people, and lanes of the road, and make a digital map in its memory. A variety of sensing technologies, including radar and lidar, enable the AV to determine its position, speed, and the locations of other objects picked out by the camera. The next stage is for it to exactly determine where it is in the world once it can "see". The next phase is to design a safe route from source A to destination B utilising perception and localization once it can properly locate itself. Last but not least, maintain control and make motions to find your route. Self-driving cars need object identification, edge detection in images, region of interest selection, road line detection, and numerous more techniques to sense their surroundings. Humans can quickly recognise items and identify road lanes from complicated landscape. These algorithms are required to teach computers how to interpret visual inputs. Machines can solve these difficulties because to deep learning and robotic automation.

Figure 3 How Self-Driving Cars work? (Source: thinking Autonomous.ai, Jermey. C)

The OpenCV Library's range of algorithms and computer vision approaches for autonomous cars are illustrated in this article. OpenCV (Open-Source Computer Vision) is a library for computer vision and machine learning that contains hundreds of optimised algorithms that are freely accessible to developers for the creation of cutting-edge computer vision applications, such as automating human driving.

Lane Detection using The Canny Edge

Road Lanes are the guiding points on the roadway, it helps drivers control and guides their vehicles while reducing traffic conflicts. In computer vision, once the position of the lanes is obtained, we can train AV to develop better control and avoid de-routing conflicts.

The Edge detection technique seeks to locate the borders or edges of objects inside photographs. An AV needs to be able to pinpoint lane lines with extreme accuracy in order to know where to travel. When we drive, we may utilise lanes as a point of reference because a machine can only recognise pictures as a collection of pixel values rather than as a whole scene. It's a little complicated for AV since the algorithm finds the colour difference or "gradient" between two adjacent pixels. When there are areas in a picture where there is a sharp shift in the intensity of pixel values, an edge is recognised. We can then trace out all of these pixels to acquire the edges since every abrupt change in the gradient image corresponds to a bright pixel. An image's numeric pixel values fall between 0 and 255.

Figure 4 Extreme change in pixel intensity values to shows edges

Finding Lane Lines – Canny edge, Hough transform, Sobel technique are different methods used to determine lane lines. They help show shapes, edges, and lines to the vehicle. Identifying lanes is a significant step in making self-driving cars a reality. By identifying lane lines with as much precision as possible, the vehicle knows where to go. In OpenCV Python, Canny Edge Detection is a multi-stage algorithm, it goes through some steps, 1. Reduce Noise -> Find intensity gradient of the image-> non-maximum suppression is applied -> Hysteresis Thresholding is applied -> output is the highlighted edges. OpenCV puts all these steps in a single function. In programming syntax, the canny edge function is represented as cv2.Canny()

Image Processing

To ensure that lane detection algorithms function correctly, photos must be processed to eliminate noise, smooth the image, sort the brightness, etc. The algorithm may fail or find fake edges everywhere due to excessive noise or erratic brightness variations. To provide clarity of colours in a picture and to distinguish an item of a particular colour, image processing entails changing the colour space, for instance, from RGB (Red, Green, and blue colour model) to HSV (Hue, Saturation, Value), or grayscale. Another mathematical filter for reducing noise in a picture is the Gaussian blur. Edge detection methods can detect edges with accuracy and precision after the picture has been smoothed and denoised. The OpenCV library provides the following programming syntax for image processing operations: cv2.cvtColor() and cv2.GaussianBlur().

Figure 6 Self-driving Car Lane detection (Source: Lane lines detector Project @realderektan)

Region of Interest

From the incoming input of images, we need to extract regions of Interest (ROI). The ROIs contain objects, pedestrians, vehicles, or other distinguishable background objects in an image. It also helps in identifying objects that may pose danger to the vehicle. In Lane detection, the area of the lane is the region of most interest. The area of the road lane is marked as our region of interest or the triangle as illustrated in the image below. This is done to remove the edges of non-interesting objects (trees, mountains, building blocks), we need only the lane lines of the roads, i.e., a very specific region of the road. The region is masked and its coordinates are specified to calculate the plot of lane lines in an image. The lane markings will move as the vehicle moves to easily detect lanes when the scenery changes. OpenCV provides many calculating operations in lane detection. Click here to learn Best Data Science Course in Hyderabad

Figure 7 Lane Line Detector (Source: Self-driving car project- Lane detector, @realderektan)

Object Detection and Classification

Finding moving items in a series of images is an intriguing concept. There are several tasks. We can do tasks like identifying automobiles, people, the quantity of items in a scene, their sizes, their distances from one another, etc. thanks to object detection algorithms. These are crucial because they help to address numerous problems that arise when creating robots that can "see." Object detection entails identifying what object is there in a picture and where it is placed. It also records the coordinates and specifics of the object. Identifying the item in an image—such as a person, a traffic light, a stop sign, an automobile, or any other in the scene—is known as object classification. To categorise photos, a convolutional neural network is used. The Sliding Windows technique, which stands for "you only look once," is another technique used for object identification and categorization. It is regarded as state-of-the-art since it can operate at extremely high frequency without missing obstacles. Developers may build and alter object detection models using the different tools and OpenCV APIs.

Figure 8 Object Detection and Classification (Source: Object Detection on Autonomous Vehicle @Neil Nie)

The use of computer vision and deep learning in self-driving cars was briefly explored in this article. It covers various fundamental OpenCV perception functions, such as Object and Lane Detection. An all-inclusive library for computer vision tasks is OpenCV. The primary goal of autonomous vehicles' intelligence is to locate objects and traffic lanes while also calculating distance, speed, and location. The approaches to computer vision and deep learning are always evolving, thus it is essential to regularly adopt new methods. The existing models may constantly be enhanced to increase accuracy and predictability, as well as to better train and create AV algorithms. Aside from deep learning for lane detection, more advanced techniques exist, such as those for steering angle prediction, anticipating when to apply the brakes, overcoming detection difficulties when there are no lane markings, and many other novel ideas that need to be investigated for the creation of intelligent self-driving cars.