Detection of Blocked Pedestrians Based on RDB-YOLOv4 in Coal Mine

doi:10.3778/j.issn.1002-8331.2009-0449

Abstract

Abstract: Aiming at the false detection and missed detection caused by pedestrians easily blocked by large equipments during the detection process in coal mines, this paper proposes the detection method of blocked pedestrians in underground coal mines based on RDB-YOLOv4 network. The algorithm is based on YOLOv4 network architecture, and joins the residuals dense block（RDB） in CSPDarknet-53 network, to achieve cross-layer transfer and integration features at different levels. The continuous connections ensure the storage and memory of low level and high-level feature information, so that complete and effective local features can accurately predict the information of blocked pedestrians. Compared with the current mainstream target detection algorithms and occlusion processing detection algorithms, the algorithm in this paper has effectively improved the average precision（AP） of the test under the PASCAL VOC 2007 open data set and underground coal mine pedestrian data set. Compared with YOLOv4, the average precision of the test in two different data sets has been improved by 2.74 percentage points and 3.5 percentage points respectively（IoU=0.5）.

Key words: blocked pedestrian detection, YOLOv4 network, residual dense block, coal mine

摘要： 针对煤矿井下数字化人员检测过程中行人易被大型设备遮挡而导致的误检、漏检等问题，提出了一种基于RDB-YOLOv4网络的煤矿井下有遮挡行人检测方法。该算法以YOLOv4为基础网络架构，在CSPDarknet-53特征提取网络中加入了残差密集块（residual dense block，RDB），对不同层次的特征实现跨层传递和融合，连续的连接保证了低级和高级特征信息的存储和记忆，使得完整有效的局部特征能准确预测被遮挡行人的信息。对比当前主流目标检测算法和遮挡处理检测算法，该算法在PASCAL VOC 2007公开数据集和煤矿井下行人数据集下有效提升了测试的平均精度（average precision，AP），相比YOLOv4在两组不同数据集测试的平均精度分别提升了2.74个百分点和3.5个百分点（IoU=0.5）。

关键词: 遮挡行人检测, YOLOv4网络, 残差密集块, 煤矿井下

XIE Binhong, YUAN Shuai, GONG Dali. Detection of Blocked Pedestrians Based on RDB-YOLOv4 in Coal Mine[J]. Computer Engineering and Applications, 2022, 58(5): 200-207.

谢斌红, 袁帅, 龚大立. 基于RDB-YOLOv4的煤矿井下有遮挡行人检测[J]. 计算机工程与应用, 2022, 58(5): 200-207.

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