Lane detection is a critical part of advanced driver assist technology (ADAS) and autonomous vehicles. A variety of state-of-the-art methods of lane detection have been proposed in recent times.
While these methods focus on recognizing the lanes using only one frame, they mostly provide unsatisfactory results when confronted with extreme circumstances.
For example, the degrading lines in the road, massive shadows, big occlusion of vehicles, etc. Practically, lane lines are considered continuous lines on road structures.
Therefore, a lane that cannot be identified within the live frame could be assumed from details of previous frames.
The importance of lane detection for road users by using data science
If we hope to have automated vehicles that can detect road lanes and not have to be supervised by humans, we must first know the procedure and the possible problems that could delay its widescale deployment.
Our understanding of driving in real-time scenarios is becoming more of a reality. All thanks to the advanced technology of high-precision optical and electronic sensors, high-precision computer vision, and efficient machine learning techniques.
Alongside other aspects of autonomous vehicles, road detection of lanes is the most important. The vehicle will know which direction to travel once the lanes’ positions are determined. Thus, it will reduce the dangers of overstepping over other lanes.
Several modern methods are currently available with smooth and advanced performance in studies. They include lane detection that uses geometric models.
Some include techniques based on deep machine learning. Certain models also identify issues that concern energy minimization, and some use certain techniques for supervised learning. Also, other techniques are there to divide roads into lanes.
Most of these methods limit their results by detecting roads from a single driving scenario frame, which results in poor performance when dealing with extreme driving conditions like huge shadows and massive line degrading in the road lane.
What were the challenges we faced?
We can’t get into the details to describe the entire process here as we’d consider this amateur research. I hope my coworkers have a data science certification and sufficient prior experience!
We wanted to achieve the real-time detection of lanes in a video. There are many ways to carry out the task of lane detection. It is possible to use techniques based on learning, like creating a deep-learning model using an annotated video data set or using an already-trained model.
However, there are easier ways to detect lanes too. We had four lanes, separated by white-colored markings. To identify the location of a lane, we needed to recognize the white markings that were on the sides of that lane. Which leads to the most important question: how do we identify the markings on the lane?
There were many additional objects to the road markings. Also, there were cars along the road, roadside barriers, streetlights, etc. The scene alters with each frame in a video, mimicking real-world driving scenarios very well.
To solve the problem of lane detection, we needed to figure out a way to eliminate unwanted objects on the road. One thing to take care of immediately was to limit the subject area. Instead of working on the whole frame, we needed to only work with the frame in a small area.
In addition to Lane markings, the rest had to be concealed within the frame. The lane markings will be placed within the lane markings area when the vehicle moves.
Image Thresholding was an option
We used Image thresholding to overcome the issues discussed in the previous paragraph.
So, what was our motivation behind this technique? Let us explain!
In this process, image thresholding will assign the pixel values of an image in grayscale one of two values that represent white and black colors according to a threshold. If you find that the exact value for a particular pixel exceeds the threshold, it gets assigned one value, and if not, it is assigned a different value. After applying thresholding to the mask, it is only possible to see lane lines in the final image. We can now easily identify these marks with the help of the Hough Line Transformation [1].
Then we applied Hough Line Transformation
Hough Transform is a technique to recognize any mathematically represented shape. For instance, it can detect geometric shapes such as rectangles, triangles, circles, or lines. We’re looking to detect Lane markings that could be visualized as lines. To master it, we highly recommend checking through a pg data science course.
Can we, therefore, expect the accuracy we’re looking for?
In these scenarios, it is possible that the lane may be predicted incorrectly or projected in an incorrect direction. Also, it could be detected in part or may not be identified. The primary reason is that the information presented by the present image frame isn’t enough.
Given that driving situations are always continuous and often similar or almost identical between two and three frames, the location that the lines of the lane within the following frames are nearly identical and closely related.
With the help of several frames from previous can be used to decide the position of the lane in the present frame. However, the lane lines may suffer degradation or deterioration due to weathering effects and weather conditions, shadows, and occlusions caused by inadequate lighting.
Our Conclusion
In our study, we looked at a straightforward method for lane detection [2]. The method did not use any models or complicated image features. Instead, our approach was based solely on certain image processing operations.
There are likely to be many situations where this method might fail. For instance, if there are no markings for lane lines or too many vehicles along the roads, this method is likely to fail.
There are better methods to deal with these issues in lane detection. We encourage you to investigate these options if the idea of autonomous cars appeals to you. Please feel free to leave a comment with any questions or feedback.
