Jian Bo-Lin, Peng Chao-Chung
Department of Aeronautics and Astronautics, National Cheng Kung University, Tainan 70101, Taiwan.
Sensors (Basel). 2017 Jun 15;17(6):1403. doi: 10.3390/s17061403.
Due to the direct influence of night vision equipment availability on the safety of night-time aerial reconnaissance, maintenance needs to be carried out regularly. Unfortunately, some defects are not easy to observe or are not even detectable by human eyes. As a consequence, this study proposed a novel automatic defect detection system for aviator's night vision imaging systems AN/AVS-6(V)1 and AN/AVS-6(V)2. An auto-focusing process consisting of a sharpness calculation and a gradient-based variable step search method is applied to achieve an automatic detection system for honeycomb defects. This work also developed a test platform for sharpness measurement. It demonstrates that the honeycomb defects can be precisely recognized and the number of the defects can also be determined automatically during the inspection. Most importantly, the proposed approach significantly reduces the time consumption, as well as human assessment error during the night vision goggle inspection procedures.
由于夜视设备的可用性对夜间空中侦察安全有直接影响,因此需要定期进行维护。不幸的是,一些缺陷不易被肉眼观察到,甚至无法被检测到。因此,本研究针对飞行员夜视成像系统AN/AVS - 6(V)1和AN/AVS - 6(V)2提出了一种新型自动缺陷检测系统。采用由清晰度计算和基于梯度的变步长搜索方法组成的自动对焦过程,实现了蜂窝状缺陷的自动检测系统。这项工作还开发了一个清晰度测量测试平台。结果表明,在检查过程中可以精确识别蜂窝状缺陷,并能自动确定缺陷数量。最重要的是,所提出的方法显著减少了时间消耗,以及夜视镜检查过程中的人为评估误差。