27-07-2012, 10:41 AM
LANE DEPARTURE SYSTEM WITH EDGE DETECTION TECHNIQUE USING HOUGH TRANSFORM
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ABSTRACT
In this paper a novel attempt has been taken to revolutionize the safety in automotive systems with the help of image processing and GPS, which aids the driver in efficient driving.
The safety feature considered is LANE DEPARTURE SYSTEM. A high speed camera scans the image of the road scene ahead at a regular instants. These images are moved to memory for further processing. Later these images go through EDGE DETECTION TECHNIQUES. This edge detection technique consists of Sobel, canny, prewitt edge detectors, which detects the lanes on the road. Then after using HOUGH TRANSFORM these lines are detected and compared with the current car position. This helps the system in placing a constant vigil on the car over its position in the lane.
VIDEO IN AUTOMOTIVE SAFETY SYSTEMS
In many ways, car safety can be greatly enhanced by video-based systems that use high-performance media processors. Because short response times are critical to saving lives, however, image processing and video filtering must be done deterministically in real time. There is a natural tendency to use the highest video frame rates and resolution that a processor can handle for a given application, since this provides the best data for decision making. In addition, the processor needs to compare vehicle speeds and relative vehicle-object distances against desired conditions—again in real time. Furthermore, the processor must interact with many vehicle subsystems (such as the engine, braking, steering, and airbag controllers), process sensor information from all these systems, and provide appropriate audiovisual output to the driver. Finally, the processor should be able to interface to navigation and telecommunication systems to react to and log malfunctions, accidents, and other problems.
LANE DEPARTURE—A SYSTEM EXAMPLE
The overall system diagram of Figure 2 is fairly straightforward, considering the complexity of the signal processing functions being performed. Interestingly, in a video-based lane departure system, the bulk of the processing is image-based, and is carried out within a signal processor rather than by an analog signal chain. This represents a big savings on the system bill-of-materials. The output to the driver consists of a warning to correct the car’s projected path before the vehicle leaves the lane unintentionally. It may be an audible “rumble-strip” sound, a programmed chime, or a voice message.
The video input system to the embedded processor must perform reliably in a harsh environment, including wide and drastic temperature shifts and changing road conditions. As the data stream enters the processor, it is transformed—in real time—into a form that can be processed to output a decision. At the simplest level, the lane departure system looks for the vehicle’s position with respect to the lane markings in the road. To the processor, this means the incoming stream of road imagery must be transformed into a series of lines that delineate the road surface.
IMAGE ACQUISITION
An important feature of the processor is its parallel peripheral interface (PPI), which is designed to handle incoming and outgoing video streams. The PPI connects without external logic to a wide variety of video converters.
For automotive safety applications, image resolutions typically range from VGA (640 × 480 pixels/image) down to QVGA (320 × 240 pixels/image). Regardless of the actual image size, the format of the data transferred remains the same—but lower clock speeds can be used when less data is transferred. Moreover, in the most basic lane-departure warning systems, only gray-scale images are required. The data bandwidth is therefore halved (from 16 bits/pixel to 8 bits/pixel) because chroma information can be ignored.
MEMORY AND DATA MOVEMENT
Efficient memory usage is an important consideration for system designers because external memories are expensive, and their access times can have high latencies. While Blackfin processors have an on-chip SDRAM controller to support the cost-effective addition of larger, off-chip memories, it is still important to be judicious in transferring only the video data needed for the application. By intelligently decoding ITU-R 656 preamble codes, the PPI can aid this “data-filtering” operation. For example, in some applications, only the active video fields are required. In other words, horizontal and vertical blanking data can be ignored and not transferred into memory, resulting in up to a 25% reduction in the amount of data brought into the system. What’s more, this lower data rate helps conserve bandwidth on the internal and external data buses.
IMAGE FILTERING
Before doing any type of edge detection, it is important to filter the image to smooth out any noise picked up during image capture. This is essential because noise introduced into an edge detector can result in false edges output from the detector.
Obviously, an image filter needs to operate fast enough to keep up with the succession of input images. Thus, it is imperative that image filter kernels be optimized for execution in the fewest possible number of processor cycles. One effective means of filtering is accomplished with a basic two-dimensional convolution operation. Convolution is one of the fundamental operations in image processing. In two-dimensional convolution, the calculation performed for a given pixel is a weighted sum of intensity values from pixels in the neighborhood of that pixel. Since the neighborhood of a mask is centered on a given pixel, the mask area usually has odd dimensions. The mask size is typically small relative to the image; a 3 × 3 mask is a common choice because it is computationally reasonable on a per-pixel basis but large enough to detect edges in an image.