基于異構(gòu)多核處理器的雙目視覺系統(tǒng)設(shè)計(jì)與實(shí)現(xiàn)
發(fā)布時(shí)間：2018-08-26 16:05
【摘要】：雙目視覺是計(jì)算機(jī)視覺領(lǐng)域的重要分支,其通過計(jì)算出同一場景下同一空間點(diǎn)在雙目圖像上的視差來恢復(fù)深度信息,這種深度信息恢復(fù)方式具有非接觸式和被動(dòng)式兩大優(yōu)點(diǎn)。近年來,隨著對(duì)雙目匹配算法的深入研究,許多算法雖然在準(zhǔn)確性上有了很大提高,但它們都以較理想的雙目圖像為前提。事實(shí)上由于現(xiàn)實(shí)成像因素的影響,常會(huì)導(dǎo)致雙目圖像在亮度等屬性上具有偏差,這就要求算法還應(yīng)具備較高的魯棒性,另外,目前準(zhǔn)確性較高的算法均具有計(jì)算量大、實(shí)時(shí)性差的特點(diǎn),使其在要求實(shí)時(shí)處理的應(yīng)用中很難運(yùn)用。SGBM(Semi-global block matching)算法是一種半全局匹配算法,具有較高的準(zhǔn)確性,但其魯棒性與實(shí)時(shí)性依然有待提高。因此本文對(duì)SGBM算法做了改進(jìn)研究和并行加速方法研究,并將改進(jìn)后的算法應(yīng)用到本文雙目測距系統(tǒng)中。針對(duì)SGBM算法中BT匹配代價(jià)對(duì)亮度偏差不夠魯棒的問題,提出了一種聯(lián)合Census匹配代價(jià)的BT-Census匹配代價(jià)。由于Census匹配代價(jià)很好的保留了鄰域像素間的結(jié)構(gòu)特性,因此在匹配時(shí)提高了對(duì)亮度偏差的魯棒性。為了提高實(shí)時(shí)性,本文對(duì)改進(jìn)后的算法做了并行加速方法的研究,并基于OpenCL(Open Computing Language)異構(gòu)并行計(jì)算框架做了實(shí)現(xiàn)。首先對(duì)該算法中匹配代價(jià)、代價(jià)優(yōu)化、視差計(jì)算及視差精細(xì)化模塊的主要算法做了并行化分析,然后在OpenCL框架下,對(duì)算法的數(shù)據(jù)存儲(chǔ)結(jié)構(gòu)、OpenCL內(nèi)核做了設(shè)計(jì),并結(jié)合內(nèi)核性能評(píng)估與算法優(yōu)化,實(shí)現(xiàn)了異構(gòu)并行加速的算法。實(shí)驗(yàn)表明,在同一處理器(AMD APU A8-4555M)上,本文算法在保證準(zhǔn)確性相近的前提下,相比于經(jīng)過SSE2指令集加速的SGBM算法,在實(shí)時(shí)性能上依然有2.2倍的提升。本文以上述改進(jìn)的算法為核心,設(shè)計(jì)與實(shí)現(xiàn)了雙目測距系統(tǒng)。該雙目測距系統(tǒng),采用了基于異構(gòu)多核處理器的計(jì)算平臺(tái),不但減小了多核間的數(shù)據(jù)傳輸延遲,而且有效的控制了系統(tǒng)功耗。實(shí)驗(yàn)表明,在雙目圖像尺寸為640×480,最大視差層級(jí)為64的條件下,該雙目測距系統(tǒng)平均測距周期為110ms,在0.5～5米以內(nèi)的深度測量誤差小于9.6%,初步實(shí)現(xiàn)了一套低功耗、近實(shí)時(shí)的雙目測距系統(tǒng)。
[Abstract]:Binocular vision is an important branch in the field of computer vision. The depth information is restored by calculating the parallax of the same space point in the same scene on the binocular image. This depth information restoration method has two advantages: contactless and passive. In recent years, with the in-depth study of binocular matching algorithms, many algorithms have greatly improved in accuracy, but they are based on a more ideal binocular image as the premise. In fact, due to the influence of realistic imaging factors, binocular images often have deviations in luminance and other attributes, which requires that the algorithms should also have higher robustness. In addition, the current algorithms with high accuracy all have a large amount of computation. Because of the poor real-time performance, it is difficult to use SGBM (Semi-global block matching) algorithm, which is a semi-global matching algorithm, in the application of real-time processing, but its robustness and real-time performance still need to be improved. Therefore, the improved SGBM algorithm and the parallel acceleration method are studied in this paper, and the improved algorithm is applied to the binocular ranging system in this paper. Aiming at the problem that the BT matching cost is not robust to the brightness deviation in the SGBM algorithm, a new BT-Census matching cost combining the Census matching cost is proposed. Because the Census match cost is very good to retain the structure characteristics of the neighborhood pixels, so the robustness of the matching algorithm is improved to the luminance deviation. In order to improve real-time performance, the improved algorithm is studied by parallel acceleration method, and implemented based on OpenCL (Open Computing Language) heterogeneous parallel computing framework. Firstly, the main algorithms of matching cost, cost optimization, parallax calculation and parallax refinement module in this algorithm are analyzed in parallel. Then, the data storage structure of the algorithm is designed under OpenCL framework. Combined with kernel performance evaluation and algorithm optimization, heterogeneous parallel acceleration algorithm is realized. Experiments show that on the same processor (AMD APU A8-4555M), the proposed algorithm can improve the real-time performance by 2.2 times compared with the SGBM algorithm accelerated by the SSE2 instruction set, on the premise that the accuracy of the algorithm is similar. In this paper, the binocular ranging system is designed and implemented based on the improved algorithm. The binocular ranging system adopts a computing platform based on heterogeneous multi-core processor, which not only reduces the data transmission delay between multi-cores, but also effectively controls the power consumption of the system. The experimental results show that under the condition that the size of binocular image is 640 脳 480 and the maximum parallax level is 64, the average ranging period of the binocular ranging system is 110 Ms, and the depth measurement error within 0.5 m is less than 9. 6. A set of low power consumption is initially realized. Near-real-time binocular ranging system.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.41
本文編號(hào)：2205431
[Abstract]:Binocular vision is an important branch in the field of computer vision. The depth information is restored by calculating the parallax of the same space point in the same scene on the binocular image. This depth information restoration method has two advantages: contactless and passive. In recent years, with the in-depth study of binocular matching algorithms, many algorithms have greatly improved in accuracy, but they are based on a more ideal binocular image as the premise. In fact, due to the influence of realistic imaging factors, binocular images often have deviations in luminance and other attributes, which requires that the algorithms should also have higher robustness. In addition, the current algorithms with high accuracy all have a large amount of computation. Because of the poor real-time performance, it is difficult to use SGBM (Semi-global block matching) algorithm, which is a semi-global matching algorithm, in the application of real-time processing, but its robustness and real-time performance still need to be improved. Therefore, the improved SGBM algorithm and the parallel acceleration method are studied in this paper, and the improved algorithm is applied to the binocular ranging system in this paper. Aiming at the problem that the BT matching cost is not robust to the brightness deviation in the SGBM algorithm, a new BT-Census matching cost combining the Census matching cost is proposed. Because the Census match cost is very good to retain the structure characteristics of the neighborhood pixels, so the robustness of the matching algorithm is improved to the luminance deviation. In order to improve real-time performance, the improved algorithm is studied by parallel acceleration method, and implemented based on OpenCL (Open Computing Language) heterogeneous parallel computing framework. Firstly, the main algorithms of matching cost, cost optimization, parallax calculation and parallax refinement module in this algorithm are analyzed in parallel. Then, the data storage structure of the algorithm is designed under OpenCL framework. Combined with kernel performance evaluation and algorithm optimization, heterogeneous parallel acceleration algorithm is realized. Experiments show that on the same processor (AMD APU A8-4555M), the proposed algorithm can improve the real-time performance by 2.2 times compared with the SGBM algorithm accelerated by the SSE2 instruction set, on the premise that the accuracy of the algorithm is similar. In this paper, the binocular ranging system is designed and implemented based on the improved algorithm. The binocular ranging system adopts a computing platform based on heterogeneous multi-core processor, which not only reduces the data transmission delay between multi-cores, but also effectively controls the power consumption of the system. The experimental results show that under the condition that the size of binocular image is 640 脳 480 and the maximum parallax level is 64, the average ranging period of the binocular ranging system is 110 Ms, and the depth measurement error within 0.5 m is less than 9. 6. A set of low power consumption is initially realized. Near-real-time binocular ranging system.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.41
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