Recognition of Excavator Operation Objects Based on BO-CNN-LSTM-Adaboost

With the continuous development of engineering machinery technology, excavators, as a key engineering equipment, have played an important role in the fields of construction and mining. Accurate identification of excavator working objects is crucial to realize their automated operation. However, traditional working object recognition methods have obvious limitations in practical applications due to problems such as complex computational models and susceptibility to lighting conditions. To meet the above challenges, this article proposes a CNN-LSTM-Adaboost neural network model based on Bayesian optimization (BO) for excavator working object recognition. The model integrates the feature extraction capability of convolutional neural networks (CNNs), the modeling advantage of long short-term memory networks (LSTM) for time series data, and the ability of Adaboost algorithm to build strong classifiers by integrating weak classifiers. At the same time, BO is used to automatically obtain the optimal hyperparameter combination. Experimental results show that compared with other comparison models, the BO-CNN-LSTM-Adaboost model has significant advantages in comprehensive performance.