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首页> 外文会议>IEEE International Conference on System Engineering and Technology >Towards Real-Time Illegal Logging Monitoring: Gas-Powered Chainsaw Logging Detection System using K-Nearest Neighbors
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Towards Real-Time Illegal Logging Monitoring: Gas-Powered Chainsaw Logging Detection System using K-Nearest Neighbors

机译：迈向实时非法日志记录监控：使用k-最近邻居的气体电锯测井检测系统

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摘要

Deforestation is exponentially depleting the planet's biodiversity and natural ecosystems at an alarming rate. This research aims to address illegal logging through realtime alerting and monitoring of suspected gas-fueled chainsaw sounds in the forest. Features were extracted from a collated nature sound dataset and trained on a supervised machine learning algorithm. The model is deployed through a microcomputer to process the chainsaw sounds through radio frequency transmission. The system has a desktop application that triggers an alarm and visualizes relevant information from the detected illegal logging activity location. The device prototype is easily-replaceable, modular, and portable and can be reconfigured to large-scale domains such as rainforests. The main contributions of this research are the improvement of alert and monitoring of illegal logging through (1) real-time and online audio analysis and detection of gas-powered chainsaws sounds through k-nearest neighbors; (2) a deployable prototype capable of listening to chainsaw sounds in the forest while buried, and (3) development of a graphical user interface for monitoring of module feedback and responses. The experimental results show that our system has an accuracy of 96.00% an F1-score of 94.34%.
机译：森林砍伐以惊人的速度指数耗尽地球的生物多样性和自然生态系统。这项研究旨在通过实时警报和监测森林中涉嫌气体燃料电锯声音的实时警报和监测来解决非法记录。从整理的自然声音数据集中提取特征，并在监督机器学习算法上培训。通过微计算机部署模型以通过射频传输来处理电锯声。该系统具有桌面应用程序，触发警报并从检测到的非法日志记录活动位置可视化相关信息。器件原型是可易于更换的，模块化和便携式的，可以重新配置到雨林等大型域。该研究的主要贡献是改善了通过K-Reallibors的实时和在线音频分析和检测到最近的邻居的空气电锯听起来的警报和监控。 （2）可以在埋地的森林中聆听电锯声音的可部署原型，并（3）开发用于监控模块反馈和响应的图形用户界面。实验结果表明，我们的系统的精度为96.00％，F1分数为94.34％。

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