Excavation Equipment Recognition Based on Novel Acoustic Statistical Features

Abstract

Excavation equipment recognition attracts increasing attentions in recent years due to its significance in underground pipeline network protection and civil construction management. In this paper, a novel classification algorithm based on acoustics processing is proposed for four representative excavation equipments. New acoustic statistical features, namely, the short frame energy ratio, concentration of spectrum amplitude ratio, truncated energy range, and interval of pulse are first developed to characterize acoustic signals. Then, probability density distributions of these acoustic features are analyzed and a novel classifier is presented. Experiments on real recorded acoustics of the four excavation devices are conducted to demonstrate the effectiveness of the proposed algorithm. Comparisons with two popular machine learning methods, support vector machine and extreme learning machine, combined with the popular linear prediction cepstral coefficients are provided to show the generalization capability of our method. A real surveillance system using our algorithm is developed and installed in a metro construction site for real-time recognition performance validation.

Underground pipeline surveillance with an algorithm based on statistical time-frequency acoustic features

“…Urban underground pipelines are crucial in daily production and life, including water supply and drainage systems, high-voltage cables, optical fiber lines for communication, natural gas transmission pipelines, etc. However, the recent massive construction of urban infrastructure in several developing countries has caused severe destruction to underground pipeline systems, leading to huge economic losses [1][2][3][4] . A statistical report from the China Association of City Planning shows that a direct loss of approximately 200 billion yuan every year is caused by pipeline damage from 2008 Tianlei Wang, Jiuwen Cao, and Jianzhong Wang are with the Artificial Intelligence Institute, and also with the Key Lab for IoT and Information Fusion Technology of Zhejiang, to 2012 [2,3] .…”

A Deep Learning‐Based Approach to Enable Action Recognition for Construction Equipment

“…In a case study of front-end loader activity recognition, certain key features are extracted and are used to train supervised machine learning classifiers. Cao et al [42] proposed a classification algorithm based on acoustics processing for four types of excavation equipment. ey developed new acoustic statistical features (short frame energy ratio, concentration of spectrum amplitude ratio, truncated energy range, and pulse interval) to characterize acoustic signals; then, based on the probability density distributions of these acoustic features, a novel classifier was proposed.…”

An Acoustic-Based Recognition Algorithm for the Unreleased Braking of Railway Wagons in Marshalling Yards

“…Zhao et al [29] developed an acoustic signal processing system to measure the running state of transformers. Cao et al [30] analyzed the acoustic features during municipal excavation to protect underground pipelines. Besides, the acoustic recognition can also be applied in the identification of military jets and heavy vehicles [31], [32].…”