Leveraging convolutional neural networks for efficient classification of heavy construction equipment

Abstract

Effective classification and detection of equipment on construction sites is critical for efficient equipment management. Despite substantial research efforts in this field, most previous studies have focused on classifying a limited number of equipment categories. Furthermore, there is a scarcity of research dedicated to heavy construction equipment. Hence, this study develops a robust Convolutional Neural Network (CNN) model to classify heavy construction machinery into 12 different types. The study utilizes a comprehensive dataset of equipment images, which was divided into three distinct subsets: 60% for training the model, 30% for validating its performance, and 10% for testing its accuracy. The model’s robustness was ensured by monitoring accuracy and loss measures during the training and validation phases. The CNN model achieved approximately 85% training accuracy with a minimum loss of 0.40. The testing phase revealed a high overall precision of 80%. The CNN model accurately classifies concrete mixer machines and telescopic handlers with an Area Under the Curve (AUC) of 0.92, however pile driving machines have a lower accuracy with an AUC of 0.83. These findings demonstrate the model’s high ability to distinguish between several types of heavy construction equipment. This paper contributes to the relatively unexplored area of classifying heavy construction equipment by providing a practical tool for automating equipment classification, leading to enhanced efficiency, safety, and maintenance protocols in construction management.