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An Area-Efficient FPGA Implementatio...

Tat, Fatih,

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An Area-Efficient FPGA Implementation of Lane and Obstacle Detection for Driving Assistance Systems = = Surus Destek Si̇stemleri̇ Ici̇n Seri̇t ve Engel Tespi̇ti̇ni̇n Alan Veri̇mli̇ FPGA Uygulamasi /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	An Area-Efficient FPGA Implementation of Lane and Obstacle Detection for Driving Assistance Systems =/ Fatih Tat.
其他題名:	Surus Destek Si̇stemleri̇ Ici̇n Seri̇t ve Engel Tespi̇ti̇ni̇n Alan Veri̇mli̇ FPGA Uygulamasi /
作者:	Tat, Fatih,
面頁冊數:	1 electronic resource (47 pages)
附註:	Source: Masters Abstracts International, Volume: 84-09.
Contained By:	Masters Abstracts International84-09.
標題:	Mean square errors. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30348269
ISBN:	9798374465662

An Area-Efficient FPGA Implementation of Lane and Obstacle Detection for Driving Assistance Systems = = Surus Destek Si̇stemleri̇ Ici̇n Seri̇t ve Engel Tespi̇ti̇ni̇n Alan Veri̇mli̇ FPGA Uygulamasi /
Tat, Fatih,

An Area-Efficient FPGA Implementation of Lane and Obstacle Detection for Driving Assistance Systems =Surus Destek Si̇stemleri̇ Ici̇n Seri̇t ve Engel Tespi̇ti̇ni̇n Alan Veri̇mli̇ FPGA Uygulamasi /Fatih Tat. - 1 electronic resource (47 pages)

Source: Masters Abstracts International, Volume: 84-09.

This thesis aims to develop the Lane and Obstacle detection subsystems of a driver assistance system on low-cost Field-Programmable Gate Array (FPGA) devices that will provide a safer and more comfortable ride for drivers. The performance parameters are system speed, computational efficiency, and working in different weather and road conditions. Recently, many studies have been carried out on systems such Driver assistant system, Adaptive cruise control, Anti-lock braking system, Automotive navigation system, Collision avoidance system, Emergency driver assistant etc. The presented thesis performs Lane and Obstacle detection by using edge detection algorithms, region of interest (ROI), and the Hough Transform (HT) method. Proposed driver support systems are implemented on Xilinx Zynq-7000 FPGA and MATLAB software platform on approximately 1400 images over seven different weather and road conditions. It has been observed that the results obtained on FPGA are up to 1322 times faster than the results obtained with MATLAB. The study showed that for 1400 frames, the Sobel operator works correctly at 1332 frames and the Prewitt operator at 1288 frames at lane detection. The Sobel filter has been proven to be 6.2% better than the Prewitt filter at lane detection and 3.3% better at Obstacle detection in result efficiency. The designed algorithm implemented in terms of Lookup Tables (LUTs) usage is 38.95% more efficient than using 1906 LUTs, with a low energy footprint. This system can process 27 frames per second by applying edge detection algorithms, ROI, and HT methods which are acceptable for real-time applications.

English

ISBN: 9798374465662Subjects--Topical Terms:

3562318
Mean square errors.

An Area-Efficient FPGA Implementation of Lane and Obstacle Detection for Driving Assistance Systems = = Surus Destek Si̇stemleri̇ Ici̇n Seri̇t ve Engel Tespi̇ti̇ni̇n Alan Veri̇mli̇ FPGA Uygulamasi /
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245 1 3 $a An Area-Efficient FPGA Implementation of Lane and Obstacle Detection for Driving Assistance Systems = $b Surus Destek Si̇stemleri̇ Ici̇n Seri̇t ve Engel Tespi̇ti̇ni̇n Alan Veri̇mli̇ FPGA Uygulamasi / $c Fatih Tat.
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520 $a This thesis aims to develop the Lane and Obstacle detection subsystems of a driver assistance system on low-cost Field-Programmable Gate Array (FPGA) devices that will provide a safer and more comfortable ride for drivers. The performance parameters are system speed, computational efficiency, and working in different weather and road conditions. Recently, many studies have been carried out on systems such Driver assistant system, Adaptive cruise control, Anti-lock braking system, Automotive navigation system, Collision avoidance system, Emergency driver assistant etc. The presented thesis performs Lane and Obstacle detection by using edge detection algorithms, region of interest (ROI), and the Hough Transform (HT) method. Proposed driver support systems are implemented on Xilinx Zynq-7000 FPGA and MATLAB software platform on approximately 1400 images over seven different weather and road conditions. It has been observed that the results obtained on FPGA are up to 1322 times faster than the results obtained with MATLAB. The study showed that for 1400 frames, the Sobel operator works correctly at 1332 frames and the Prewitt operator at 1288 frames at lane detection. The Sobel filter has been proven to be 6.2% better than the Prewitt filter at lane detection and 3.3% better at Obstacle detection in result efficiency. The designed algorithm implemented in terms of Lookup Tables (LUTs) usage is 38.95% more efficient than using 1906 LUTs, with a low energy footprint. This system can process 27 frames per second by applying edge detection algorithms, ROI, and HT methods which are acceptable for real-time applications.
520 $a Bu tez, suruculer icin daha guvenli ve konforlu bir surus saglayacak dusuk maliyetli Alanda Programlanabilir Kapi (FPGA) cihazlari uzerinde bir surucu yardim sisteminin Serit ve Engel algilama alt sistemlerini gelistirmeyi amaclamaktadir. Tasarlanan algoritmada baslica hedefler daha hizli, verimli, farkli hava kosullarinda ve farkli yol kosullarinda calisabilen bir sistem olusturmaktir. Son zamanlarda Surucu asistan sistemi, Uyarlanabilir hiz sabitleyici, Kilitlenme onleyici fren sistemi, Otomotiv navigasyon sistemi, Carpisma onleme sistemi, Acil durum surucu asistani vb. gibi benzer sistemlerle ilgili bircok calisma yapilmistir. Bu tez calismasinda kenar algilama algoritmalarini, ilgi alani yontemini (ROI) ve Hough donusumunu (HT) kullanarak serit takip ve nesne belirlemeyi gerceklestirmektedir. Onerilen surucu destek sistemleri Xilinx Zynq-7000 FPGA ve MATLAB yazilim platformu uzerinde 7 farkli hava ve yol kosulu uzerinde yaklasik 1400 gorsel uzerinde test edilmistir. FPGA uzerinde elde edilen sonuclarin MATLAB ile elde edilen sonuclara gore 1322 kata kadar daha hizli oldugu gozlemlenmistir. 1400 gorsel icin test edilen Sobel operatorunun 1332 gorsel ve Prewitt operatorunun 1288 gorsel uzerinde serit belirlemede dogru calistigini gostermistir. Sobel filtresinin sonuc dogrulugu olarak Prewitt filteresine gore serit belirlemede %6.2 ve engel algilamada %3.3 daha iyi oldugu kanitlanmistir. Look up Table (LUT) kullanimi acisindan tasarlanan algoritma 1906 LUT kullanarak %38.95 daha verimlidir. Bu sistem, gercek zamanli uygulamalar icin kabul edilebilir olup kenar algilama algoritmalari, ilgi alani yonetimi ve Hough Donusumu yontemlerini uygulayarak saniyede 27 kare isleyebilir.
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