目前基于深度学习的煤矿井下目标检测算法在面对光照强度分布不均、目标环境复杂及多类目标尺度分布不均衡时，对复杂小目标的检测效果不佳，易出现漏检和误检现象。针对上述问题，基于单阶段目标检测算法YOLOv8n，提出了一种基于动态蛇形卷积的特征提取（FEDSC）−双向特征金字塔网络与语义和细节融合的特征融合（FFBD）的煤矿井下多目标检测算法，即采用FEDSC替换YOLOv8n的主干网络，扩大感受野；将FFBD作为颈部网络，减少目标误检和漏检；引入SIoU的解耦检测头作为检测层，提高模型对小目标的适应能力与模型收敛速度。实验结果表明：① FEDSC−FFBD算法的mAP@0.5为97.00%，模型参数量为4.22×10⁶个，每秒浮点运算数为21.7×10⁹。② FEDSC−FFBD算法的mAP@0.5较YOLOv8n算法提升了3.40%，对安全帽小目标的识别准确率为90.90%，较YOLOv8n算法提升了11%。③ 与其他YOLO系列算法相比，FEDSC−FFBD算法的mAP@0.5最高，较YOLOv5s，YOLOv9c，YOLOv10n和YOLOv11n算法分别提升了3.60%，1%，10.50%和6.40%。④ FEDSC−FFBD算法在面对煤矿井下光照强度分布不均、目标环境复杂及尺度分布不均衡的条件下，提高了多类别目标的检测精度，改善了小目标漏检和误检的问题。基于FEDSC−FFBD的煤矿井下多目标检测算法在无图像质量增强算法的前提下，克服了光照强度分布不均对小尺度目标检测带来的挑战。

Currently, underground coal mine target detection algorithms based on deep learning show poor performance in detecting complex small targets under conditions of uneven light intensity distribution, complex target environments, and imbalanced multi-class target scale distribution, often resulting in missed detection and false detection. To address these issues, based on the single-stage target detection algorithm YOLOv8n, this study proposed an underground coal mine multi-target detection algorithm based on feature extraction by dynamic snake convolution (FEDSC)-feature fusion by bi-directional feature pyramid network and semantic and detail fusion (FFBD). FEDSC replaced the backbone network of YOLOv8n to expand the receptive field, while FFBD acted as the neck network to reduce target false detection and missed detection. Additionally, a decoupling detection head of SIoU was used as the detection layer to improve the model's adaptability to small targets and the convergence speed. The results showed that: ① The mAP@0.5 of the FEDSC-FFBD algorithm was 97.00%, the number of model parameters was 4.22×10⁶, and the number of floating point operations per second was 21.7×10⁹. ② The mAP@0.5 of the FEDSC-FFBD alorithm was 3.40% higher than the YOLOv8n algorithm, and the recognition accuracy of the helmet small target was 90.90%, 11% higher than the YOLOv8n algorithm. ③ Compared with other YOLO series algorithms, the FEDSC-FFBD algorithm achieved the highest mAP@0.5, which was 3.60%, 1%, 10.50%, and 6.40% higher than YOLOv5s, YOLOv9c, YOLOv10n, and YOLOv11n algorithms, respectively. ④ The FEDSC-FFBD algorithm improved the detection accuracy of multi-class targets and reduced missed detection and false detection of small targets under conditions of uneven light intensity distribution, complex target environments, and imbalanced target scale distribution in underground coal mine. The underground coal mine multi-target detection algorithm based on FEDSC-FFBD overcame the challenge of small-scale target detection caused by uneven light intensity distribution without relying on image quality enhancement algorithms.