针对采掘工作面目标尺度跨度大、多目标间相互遮挡严重及恶劣环境导致的检测精度降低等问题，提出了一种基于FBEC−YOLOv5s的采掘工作面多目标检测算法。首先，在主干网络引入FasterNet网络，以凭借其残差连接与批标准化模块，增强模型的特征提取和语义信息捕捉能力；其次，在YOLOv5s模型颈部融合BiFPN网络，以通过其双向跨尺度连接和快速归一化融合操作，实现多尺度特征的快速捕捉与融合；最后，采用ECIoU损失函数代替CIoU损失函数，以提升检测框定位精度和模型收敛速度。实验结果表明：① 在满足煤矿井下实时检测要求的同时，FBEC−YOLOv5s模型的准确率较YOLOv5s模型的准确率提升了3.6%。② 与YOLOv5s模型相比，FBEC−YOLOv5s模型的平均检测精度均值上升了2.8%，平均检测精度均值为92.4%，能够满足实时检测要求。③ FBEC−YOLOv5s模型的综合检测性能好，能够在恶劣环境、多目标间相互遮挡严重及目标尺度跨度大导致检测精度降低的情况下表现出良好的实时检测能力且具有较好的鲁棒性。

A multi object detection algorithm based on FBEC-YOLOv5s is proposed to address the issues of reduced detection precision caused by large object scale spans, severe obstruction between multiple objects, and harsh environments in mining faces. Firstly, the FasterNet network is introduced into the backbone network to enhance the model's feature extraction and semantic information capture capabilities through its residual connection and batch standardization module. Secondly, the BiFPN network is fused in the neck of the YOLOv5s model to achieve rapid capture and fusion of multi-scale features through its bidirectional cross scale connection and fast normalization fusion operation. Finally, the ECIoU loss function is used instead of the CIoU loss function to improve the positioning precision of the detection frame and the convergence speed of the model. The experimental results show the following points. ① While meeting the real-time detection requirements of coal mines, the precision of the FBEC-YOLOv5s model has increased by 3.6% compared to YOLOv5s model. ② Compared with the YOLOv5s model, the average detection precision of the FBEC-YOLOv5s model has increased by 2.8%, with an average detection precision of 92.4%, which can meet real-time detection requirements. ③ The FBEC-YOLOv5s model has good comprehensive detection performance, demonstrating good real-time detection capability and robustness in condition that detection accuracy is reduced caused by harsh environments, severe mutual obstruction between multiple objects, and large object scale spans.