Visual Detection Of Construction Equipment Emission

3081 Words May 17th, 2015 13 Pages
Visual Detection Of Construction Equipment Emission
Yi Liu

Abstract
Construction industry has great impact in the environment, especially in the pollutant gas emission. Measuring the emission is a vital part for us to take actions in order to enhance the compliance with environmental regulations and achieve the environmental goals. Most of the previous researches only focus on the operation phase of emission instead of the construction phase. But the fact is that more than 50% of the emissions come from the construction site equipment. In this paper, a new method has been presented to help solve the problem by visual monitoring the equipment activity and calculating the total emission based on the emission factor. In order to realize
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In this paper, the first section will be the overview of the industry and related previous work. Next, the possible solution will be presented including the feature localization and recognition. It is also accompanied with the experiment test of the algorithm. After that, the emission factor of different equipment activity will be gathered and presented. The potential benefits and limitation of the method will be discussed afterwards. Conclusion and future development will be in the last section.

2. Background and related work
2.1 Equipment recognition and tracking
Many construction companies are still using the tradition ways, including the crew balance chart, the five minutes rating and etc., to observe the site productivity[12]. Although these methods are beneficial to improve the performance, the large amount manual data collection is time-consuming[1] and subjective[2]. This kind of limitation will prevent the opportunities for continuous accurately monitoring, which is a key element in productivity improvement[3]. As a result, more and more companies start implementing the new techniques to realize the automatically monitoring the construction site instead the manual work[12].
To meet the needs, different technologies such as RFID tags, Ultra WideBand (UWB), 3D range imaging cameras, global and local positioning systems (GPS), and computer vision techniques have been explored[12]. Teizer et al. [4] implemented the UWB technology to track the
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