矿业科学学报2025，Vol.10Issue(2)：316-327,12.DOI:10.19606/j.cnki.jmst.2024907
基于YOLOv8-ECW的井下人员行为实时检测算法
Real-time detection algorithm of underground personnel behavior based on YOLOv8-ECW
摘要
Abstract
The existing models for detecting the behaviors of personnel in coal mine wells suffer from is-sues such as low accuracy and significant computational load.Therefore,a real-time detection algo-rithm for the behaviors of underground personnel based on YOLOv8-ECW is proposed.Based on YOLOv8n,the backbone network is enhanced by presenting the multi-scale convolution module EMSC.It is combined with the C2f convolution to design the C2f_EMSC module,effectively capturing the multi-scale features of the target and reducing the computational volume and parameter quantity of the model.The CGBlock downsampling module is introduced into the network to fuse the global context in-formation.The WIoU loss function is incorporated to enhance the positioning accuracy of the detection box and the convergence speed of the model.Experiments conducted on the self-established dataset for detecting the behaviors of personnel in coal mines reveal the following results:① Compared with the baseline YOLOv8n model,the average precision mean(mAP50)of the YOLOv8-ECW model for vari-ous targets is 92.4％,an increase of 2.1％;and the mAP50-95 is 75.4％,an increase of 4.0％.②The detection speed of the YOLOv8-ECW is 238 frames per second,which is 5 frames per second high-er than that of the YOLOv8n model.③ Compared with the mainstream network models such as YOLOv6 and YOLOv7,the detection performance of the YOLOv8-ECW model is the best and it exhib-its better robustness.关键词
煤矿井下/YOLOv8/行为检测/C2f_EMSC/WIoU/特征融合Key words
coal mine underground/YOLOv8/behavior detection/C2f_EMSC/WIoU/feature fusion分类
矿业与冶金引用本文复制引用
骆津津,陈伟,田子建,张帆,刘毅..基于YOLOv8-ECW的井下人员行为实时检测算法[J].矿业科学学报,2025,10(2):316-327,12.基金项目
国家自然科学基金(52274160,51874300,52074305,52374165,52121003) （52274160,51874300,52074305,52374165,52121003）
