A deep hybrid learning model to detect unsafe behavior: Integrating convolution neural networks and long short-term memory
MetadataShow full item record
Computer vision and pattern recognition approaches have been applied to determine unsafe behaviors on construction sites. Such approaches have been reliant on the computation of artificially complex image features that utilize a cumbersome parameter re-adjustment process. The creation of image features that can recognize unsafe actions, however, poses a significant research challenge on construction sites. This due to the prevailing complexity of spatio-temporal features, lighting, and the array of viewpoints that are required to identify an unsafe action. Considering these challenges, a new hybrid deep learning model that integrates a convolution neural network (CNN) and long short-term memory (LSTM) that automatically recognizes workers' unsafe actions is developed. The proposed hybrid deep learning model is used to: (1) identify unsafe actions; (2) collect motion data and site videos; (3) extract the visual features from videos using a CNN model; and (4) sequence the learning features that are enabled by the use of LSTM models. An experiment is used to test the model's ability to detect unsafe actions. The results reveal that the developed hybrid model (CNN + LSTM) is able to accurately detect safe/unsafe actions conducted by workers on-site. The model's accuracy exceeds the current state-of-the-art descriptor-based methods for detecting points of interest on images.
Showing items related by title, author, creator and subject.
A deep learning-based approach for mitigating falls from height with computer vision: Convolutional neural networkFang, W.; Zhong, B.; Zhao, N.; Love, Peter; Luo, H.; Xue, J.; Xu, S. (2019)Structural supports (e.g., concrete and steel) provide engineering structures with stability by transferring loads. During the construction of an engineering structure, individuals are often prone to taking short take-cuts ...
Automated detection of workers and heavy equipment on construction sites: A convolutional neural network approachFang, W.; Ding, L.; Zhong, B.; Love, Peter; Luo, H. (2018)© 2018 Elsevier Ltd Detecting the presence of workers, plant, equipment, and materials (i.e. objects) on sites to improve safety and productivity has formed an integral part of computer vision-based research in construction. ...
Nugraheni, Fitri (2008)This thesis sets out research carried out to investigate the usefulness of a descriptive database of construction methods for safety assessment. In addition, it investigates the possibility of utilising construction images ...