A Study Of Car Detection In Highway With High Resolution Aerial Photo

Car detection is a very important task of intelligent transportation system (ITS). And it also has drawn broad attention of research community in computer vision for many years. With the fast development of city, cars increased sharply, leading to jamming very heavily. Thus, how to ensure road net harmonize with the development of city, how to ensure the comprehensive and timely investigation data about transportation, optimize the road net, achieve the reasonable distribution of road net, make it play a full and efficient role.High resolution aerial photo is very affluent in spatial information. Car detection can provide the necessary information for the roads transportation planning organization to realize the reasonable and true intelligent transportation.After image mosaicing of high resolution aerial photos and clipping the typical highway, we utilize maximal variance, edge detection, template matching, gray scale mathematical morphology and two-valued mathematical morphology methods to explore car detection. Our mainly job is listed below.(1) We make use of maximal variance to get the optimal threshold to binarize highway image. Then, we utilize two-valued mathematical morphology to detect cars. The experiment shows that the method has high precise rate for highway with simple background but the precise rate is very low for highway with complex background.(2) We utilize five edge detection arithmetic operators to binarize highway image. Then, we make use of two-valued mathematical morphology on binary images to detect cars. The experiment shows that the edge continuity with Robert operator is worse than Sobel and Prewitt operators. The edge continuity with Canny operator is best than the others.But car detection results indicate that edge detection based on Sobel operator has best precise rate than other operators for highway with simple background. Car detection with Sobel and Canny operators has better results for highway with complex background, but the precise rate is very low for highway with complex background.(3) We also utilize template matching to detect cars in highway. But the experiment shows that because the aerial photo has very high resolution, the detail is very clear, to detect cars in highway, we have to establish exhaustive templates of different brands and models. At the same time, template matching has exhaustive program calculation, because the correlation coefficient will cost plenty of hours.(4) Finally, we make use of gray scale mathematical morphology and two-valued mathematical morphology to detect cars in highway.For the light background, after gray scale morphological top-hat filtering and morphological opening on the highway image, computing the global threshold of top-hat image, we utilize the global threshold to convert the opening image to a binary image, by sieving the bigger and smaller ground objects, the cars can be detected from light background. For the dark background, after gray scale morphological bot-hat filtering and morphological closing on the highway image, computing the global threshold of bot-hat image, we utilize the global threshold to convert the closing image to a binary image, by sieving the smaller ground objects, the cars can be detected from dark background.At last, we overlay the car detection results and eliminate the repeated detection results. The experiment shows that the harmonic mean (Fm) is up to 94%. Thus, the method is very robust.Comparing with maximal variance and edge detection methods, gray scale mathematical morphology and two-valued mathematical morphology method can get higher harmonic mean about car detection. Gray scale mathematical morphology and two-valued mathematical morphology method cost a bit more time than the former, but it is more robust. Comparing with template matching, gray scale mathematical morphology and two-valued mathematical morphology method cost less time, and it is also more efficient and robust.