为解决煤矿井下无人驾驶电机车由于光照不均、高噪声等复杂环境因素导致的多目标检测精度低及小目标识别困难问题，提出一种基于SD−YOLOv5s−4L的煤矿井下无人驾驶电机车多目标检测模型。在YOLOv5s基础上进行以下改进，构建SD−YOLOv5s−4L网络模型：引入SIoU损失函数来解决真实框与预测框方向不匹配的问题，使得模型可以更好地学习目标的位置信息；在YOLOv5s头部引入解耦头，增强网络模型的特征融合与定位准确性，使得模型可以快速捕捉目标的多尺度特征；引入小目标检测层，将原三尺度检测层增至4层，以增强模型对小目标的特征提取能力和检测精度。在矿井电机车多目标检测数据集上进行实验，结果表明：SD−YOLOv5s−4L网络模型对各类目标的平均精度均值（mAP）为97.9%，对小目标的平均检测精度（AP）为98.9%，较YOLOv5s网络模型分别提升了5.2%与9.8%；与YOLOv7，YOLOv8等其他网络模型相比，SD−YOLOv5s−4L网络模型综合检测性能最佳，可为实现矿井电机车无人驾驶提供技术支撑。

Due to complex environmental factors such as uneven illumination and high noise, unmanned electric locomotives in coal mines have low accuracy in multi object detection and difficulty in recognizing small objects. In order to solve the above problems, a multi object detection model for underground unmanned electric locomotives in coal mines based on SD-YOLOv5s-4L is proposed. On the basis of YOLOv5s, the following improvements are made to construct the SD-YOLOv5s-4L network model. The model introduces the SIoU loss function to solve the problem of mismatch between the direction of the real box and the predicted box, so that the model can better learn the position information of the object. The model introduces decoupled heads at the head of YOLOv5s to enhance the feature fusion and positioning accuracy of the network model. It enables the model to quickly capture multi-scale features of the object. The model introduces a small object detection layer to increase the original three scale detection layer to four scale. It enhances the model's feature extraction capability and detection precision for small objects. The experiment is conducted on a multi object detection dataset of the mine electric locomotives. The results show the following points. The mean average precision (mAP) of the SD-YOLOv5s-4L network model for various types of objects is 97.9%, and the average precision (AP) for small objects is 98.9%. Compared with the YOLOv5s network model, it improves by 5.2% and 9.8%, respectively. Compared with other network models such as YOLOv7 and YOLOv8, the SD-YOLOv5s-4L network model has the best comprehensive detection performance and can provide technical support for achieving unmanned driving of the mine electric locomotives.