We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
A deep learning approach for construction vehicles fill factor estimation and bucket detection in extreme environments.
- Authors
Guan, Wei; Chen, Zeren; Wang, Shuai; Wang, Guoqiang; Guo, Jianbo; Liu, Zhengbin
- Abstract
The development of autonomous detection technology is imperative in the field of construction. The bucket fill factor is one of the main indicators for evaluating the productivity of construction vehicles. Bucket detection is a prerequisite for bucket trajectory planning. However, previous studies have been conducted under ideal environments, a specific single environment, and several normal environments without considering the actual harsh environments at construction sites. Therefore, seven extreme environments are set in this paper to fill this gap, and an effective method is proposed. First, a novel framework for image restoration under extreme environments is proposed. It applies to all tasks conducted by vision on construction sites. Second, a combination of segmentation and classification networks is used for the first time in this area. Multitask learning is used to discover a positive correlation between fill factor estimation and bucket detection. Furthermore, probabilistic methods and transfer learning were introduced, and excellent results were achieved (97.40% accuracy in fill factor estimation and 99.76% accuracy in bucket detection for seven extreme environments).
- Subjects
EXTREME environments; DEEP learning; IMAGE reconstruction; PAILS; BUILDING sites
- Publication
Computer-Aided Civil & Infrastructure Engineering, 2023, Vol 38, Issue 13, p1857
- ISSN
1093-9687
- Publication type
Article
- DOI
10.1111/mice.12952