多尺度特征融合的煤矿救援机器人目标检测模型

Object detection model of coal mine rescue robot based on multi-scale feature fusion

传统目标检测模型采用人工设计的目标特征,造成检测精度较差。基于深度学习的目标检测模型具有较高的检测精度,然而针对实时性和精度要求比较高的煤矿救援机器人应用场合,获取的图像信息较少且目标特征不明显,造成目标检测效果较差。为提高目标检测精度和速度,基于YOLO V3模型提出了一种多尺度特征融合的煤矿救援机器人目标检测模型。该模型主要包括特征提取和特征融合2个模块:特征提取模块采用空洞瓶颈和多尺度卷积获得更加丰富的图像特征信息,增强目标特征表达能力,提高了目标分类精度和检测速度;特征融合模块在特征金字塔中引入空间注意力机制,对含有丰富语义信息的高层特征图和含有丰富位置信息的低层特征图进行有效融合,弥补了高层特征图位置信息表达能力不足的缺点,提高了目标定位精度。将该模型部署在煤矿救援机器人嵌入式NVIDIA Jetson TX2平台上进行灾后环境目标检测实验,检测精度为88.73%,检测速度为28帧/s,满足煤矿救援机器人目标检测的实时性和精度需求。

Traditional object detection model uses artificial object features,resulting in poor detection accuracy.Object detection model based on deep learning has high detection accuracy.However,for application of coal mine rescue robot with high real-time and accuracy requirements,the obtained image information is less and object features are not obvious,resulting in poor object detection effect.In order to improve accuracy and speed of object detection,on the basis of YOLO V3 model,an object detection model of coal mine rescue robot based on multi-scale feature fusion is proposed.The model mainly includes feature extraction module and feature fusion module.The feature extraction module uses hole bottleneck and multi-scale convolution to obtain more abundant image feature information,so as to enhance expression ability of object feature and improve object classification accuracy and detection speed.The feature fusion module introduces spatial attention mechanism into feature pyramid to effectively fuse high-level feature map with rich semantic information and low-level feature map with rich location information,which makes up for lack of position information expression ability of high-level feature map,and improves object positioning accuracy.The model is deployed on embedded NVIDIA Jetson TX2 platform in coal mine rescue robot for post-disaster environment object detection experiment.The detection accuracy is 88.73%and detection speed is 28 frames per second,which meet real-time and precision requirements of object detection of coal mine rescue robot.