基于人机协作的高质量城市图像采集方法

doi:10.3969/j.issn.0255-8297.2023.05.007

Abstract

Abstract: Many smart city applications require various photos collected in urbans. In this paper, contexts of sensing-task photos collected by people are converted into task instruction to drive the robot to collect more urban photos. The influence of parameters such as distance between people and objects, person’s photographing posture, and land slope on sensing effectiveness is evaluated to select appropriate task parameter values. To address deviation of task command conversion during human-machine cooperation, we present approximate nearest neighbor with dynamic threshold (DTANN) algorithm and propose two methods for optimizing task command parameters including camera posture and robot positions. The overlap of photos collected by humans and machines is used as the quality indicator to evaluate the tasks completion of the robot. Experimental results show that with the aid of DTANN method and task instruction optimization, the precision rate and recall rate both have been improved, and the F1-measure value is increased by 8% to 20%.

Key words: human-machine cooperation, robot, data driven, high quality urban photo collection, task’s instruction optimization

CLC Number:

TP391

CHEN Huihui, ZHONG Weizhao. High-Quality Urban Photos Collection Method Based on Human-Machine Cooperation[J]. Journal of Applied Sciences, 2023, 41(5): 801-814.

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High-Quality Urban Photos Collection Method Based on Human-Machine Cooperation

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