A deep learning approach for construction vehicles fill factor estimation and bucket detection in extreme environments

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).

Shovel point optimization for unmanned loader based on pile reconstruction

“…The loader is a critical construction vehicle in earthmoving operations and is valued for its efficiency, adaptability, and flexibility. The implementation of autonomous loader operations can enhance operational efficiency and safety while addressing challenges such as low driver motivation (Dadhich et al., 2016; Guan et al., 2022; Halbach et al., 2019). Shovel point selection involves providing a suitable position and heading for the autonomous loader to shovel on the target pile.…”