基于YOLOv7−SE的煤矿井下场景小目标检测方法

A small object detection method for coal mine underground scene based on YOLOv7-SE

目前的小目标检测方法虽然提高了小目标检测效果,但针对的多为常规场景,而煤矿井下环境恶劣,在井下小目标检测过程中存在小目标特征信息提取困难的问题。针对上述问题,提出了一种基于YOLOv7−SE的煤矿井下场景小目标检测方法。首先,将模拟退火(SA)算法与k−means++聚类算法融合,通过优化YOLOv7模型中初始锚框值的估计,准确捕捉井下小目标;然后,在YOLOv7骨干网络中增加新的检测层得到井下小目标高分辨率特征图,减少大量煤尘对井下小目标特征表示的干扰;最后,在骨干网络中的聚合网络模块后引入双层注意力机制,强化井下小目标的特征表示。实验结果表明:①YOLOv7−SE网络模型训练后的损失函数值稳定在0.05附近,说明YOLOv7−SE网络模型参数设置合理。②基于YOLOv7−SE网络模型的安全帽检测平均精度(AP)较Faster R−CNN,RetinaNet,CenterNet,FCOS,SSD,YOLOv5,YOLOv7分别提升了13.86%,25.3%,16.13%,12.71%,15.53%,11.59%,12.20%。基于YOLOv7−SE网络模型的自救器检测AP较Faster R−CNN,RetinaNet,CenterNet,FCOS,SSD,YOLOv5,YOLOv7分别提升了12.37%,20.16%,15.22%,8.35%,19.42%,9.64%,7.38%。YOLOv7−SE网络模型的每秒传输帧数(FPS)较Faster R−CNN,RetinaNe,CenterNet,FCOS,SSD,YOLOv5分别提升了42.56,44.43,31.74,39.84,22.74,23.34帧/s,较YOLOv7下降了9.36帧/s。说明YOLOv7−SE网络模型保证检测速度的同时,有效强化了YOLOv7−SE网络模型对井下小目标的特征提取能力。③在对安全帽和自救器的检测中,YOLOv7−SE网络模型有效改善了漏检和误检问题,提高了检测精度。

Although current small object detection methods have improved the detection performance,they are mostly objected at conventional scenarios.In harsh underground environments in coal mines,there are difficulties in extracting small object feature information during the underground small object detection process.In order to solve the problem.a small object detection method for coal mine underground scenes based on YOLOv7-SE has been proposed.Firstly,the simulated annealing(SA)algorithm is integrated with the k-means++clustering algorithm to accurately capture small underground objects by optimizing the estimation of initial anchor box values in the YOLOv7 model.Secondly,a new detection layer is added to the YOLOv7 backbone network to obtain high-resolution feature maps of underground small objects,reducing the interference of a large amount of coal dust on the feature representation of underground small objects.Finally,a dual layer feature representation of small underground objects.The experimental results show the following points.①The loss function of the YOLOv7-SE network model after training is stable around 0.05,indicating that the parameter settings of the YOLOv7-SE network model are reasonable.②The average precision(AP)of helmet detection based on the YOLOv7-SE network model has improved by 13.86%,25.3%,16.13%,12.71%,15.53%,11.59%and 12.20%compared to Faster R-CNN,RetinaNet,CenterNet,FCOS,SSD,YOLOv5 and YOLOv7,respectively.The self rescue device detection AP based on the YOLOv7-SE network model has improved by 12.37%,20.16%,15.22%,8.35%,19.42%,9.64%and 7.38%compared to Faster R-CNN,RetinaNet,CenterNet,FCOS,SSD,YOLOv5 and YOLOv7,respectively.The frames per second(FPS)of the YOLOv7-SE network model has increased by 42.56,44.43,31.74,39.84,22.74 and 23.34 frames/s compared to Faster R-CNN,RetinaNe,CenterNet,FCOS,SSD and YOLOv5,respectively,and decreased by 9.36 frames/s compared to YOLOv7.The YOLOv7-SE network model effectively enhances the feature extraction capability of the YOLOv7-SE network model for small underground objects while ensuring detection speed.③In the detection of safety helmets and self rescue devices,the YOLOv7-SE network model effectively improves missed and false detection,and improves detection precision.