Abstract: 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.