首页|智能行车记录仪图像去雾系统的FPGA设计

智能行车记录仪图像去雾系统的FPGA设计

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雾霾天气下,交通道路能见度低,导致所采集到的视频画面退化、图像信息模糊,同时考虑传统系统处理实时性不高等问题,基于ZYNQ平台设计了一种图像去雾系统,并应用于智能行车记录仪系统中。首先,针对传统暗通道去雾算法在天空区域存在失真等问题,提出了 一种分割天空区域的策略来修正图像复原参数;然后,针对计算全局大气光值时,需对整幅图像的像素排序消耗大量资源的问题,利用现场可编程门阵列(FPGA)并行运算的优势,提出一种帧迭代方法优化求取大气光值,同时优化了引导滤波的硬件设计;最后,将双路高清多媒体接口(HDMI)资源中,一路作为视频输入,另一路作为视频处理输出,搭建实时交通图像视频处理试验平台。试验结果表明,系统针对雾霾天气下的交通视频具有较好的去雾效果,尤其是可以解决天空区域去雾的失真问题。在对分辨率为1 280像素× 720像素的交通视频去雾时,可以达到30帧/s的处理速度,满足实时性要求。
FPGA Design of Image Defogging System in Intelligent Tachograph
In hazy weather,due to the low visibility of traffic roads,the video images collected are degraded and the image information is fuzzy.Considering the problem of low real-time processing of the traditional system,an image defogging system based on the ZYNQ platform is designed and applied to the intelligent driving recorder system.First,aiming at the problems of the traditional dark channel defogging algorithm in the sky region,a sky region segmentation strategy is proposed to correct the image restoration parameters.Then,in order to solve the problem that the pixel ordering of the whole image consumes a lot of resources when calculating the global atmospheric light value,a frame iteration method is proposed to optimize the atmospheric light value by making the advantage of the parallel operation on the FPGA platform,and at the same time,the optimization of guided filtering is realized.Finally,using dual-channel high definition multimedia interface(HDMI)resources,a real-time traffic image video processing experimental platform is established by using one channel of the dual HDMI resources as video input and the other as video processing output,and experimental simulation of the algorithm in this paper is conducted.The experimental results show that the system has a good defogging performance for the traffic video in hazy weather,especially in solving the problem of defogging distortion in the sky area.When the traffic video with a resolution of 1 280X 720 is defogged,the processing speed can reach 30 frame/s meeting the real-time requirements.

traffic videoimage defoggingZYNQ platformreal-time processing

黄鹤、胡凯益、杨澜、王浩、高涛、王会峰

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长安大学智慧高速公路信息融合与控制西安市重点实验室,西安 710064

长安大学电子与控制工程学院,西安 710064

长安大学信息工程学院,西安 710064

交通视频 图像去雾 ZYNQ平台 实时处理

国家重点研发计划国家自然科学基金面上项目国家自然科学基金面上项目陕西省重点研发计划西安市智慧高速公路信息融合与控制重点实验室(长安大学)开放基金中央高校基本科研业务费资助项目

2021YFB250120052172379521723242021SF-483300102323502300102323501

2024

上海交通大学学报
上海交通大学

上海交通大学学报

CSTPCD北大核心
影响因子:0.555
ISSN:1008-7095
年,卷(期):2024.58(4)
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