基于图像融合和改进CornerNet-Squeeze的煤矿井下行人检测方法

A pedestrian target detection method for underground coal mine based on image fusion and improved CornerNet-Squeeze

  • 摘要: 在煤矿井下无人驾驶和安防监控等领域,对行人目标的检测至关重要,但受井下光线昏暗、光照不均、背景复杂、行人目标小且密集等特殊工况环境的影响,图像中的行人目标存在边缘细节特征少、信噪比低、与背景相似度高等问题,难以有效识别遮挡多尺度下的行人目标。针对上述问题,提出了一种基于图像融合和改进CornerNet-Squeeze的煤矿井下行人目标检测方法。采用双尺度图像融合(TIF)算法将红外相机和深度相机采集的图像进行像素级融合,再进行形态学处理,减少背景干扰。在CornerNet-Squeeze网络基础上,将八度卷积(OctConv)引入沙漏型主干网络,处理图像特征中高低频信息,增强图像边缘特征,提高多尺度行人检测能力。实验结果表明:① 在深度图像、红外图像、融合图像3种数据集上,改进CornerNet-Squeeze模型在保持原算法实时性的同时,有效提升了井下行人检测精度。② 采用融合图像数据集训练的模型检测精度较红外图像和深度图像数据集训练的模型高,可见融合图像能充分发挥深度图像和红外图像的优势,有助于提高模型检测精度。③ 在不同程度遮挡和多尺度行人目标6种场景下,改进CornerNet-Squeeze训练的模型的行人漏检率最低。④ 与YOLOv4 相比,在 COCO2014 行人数据集上改进CornerNet-Squeeze算法的平均精度提高了 1.1%,检测速度提高了6.7%。⑤ 改进CornerNet-Squeeze能够有效检测出图像中远处小目标,对小目标的检测能力提升明显。

     

    Abstract: In unmanned driving and security monitoring in the coal mine, detecting pedestrian targets is very important. But under the influence of special working conditions such as dim light, uneven illumination, complex background, and small and dense pedestrian targets, the pedestrian targets in the image have some problems such as few edge details, low signal-to-noise ratio and high similarity with the background. It is difficult to effectively identify the pedestrian targets under multi-scale occlusion. In order to solve the above problems, a pedestrian detection method for underground coal mine based on image fusion and improved CornerNet-Squeeze is proposed. The image collected by the infrared camera and depth camera is fused at the pixel level using the two-scale image fusion (TIF) algorithm. The morphological processing is carried out for the fused imoge to reduce background interference. Based on the CornerNet-Squeeze network, octave convolution (OctConv) is introduced into the hourglass type backbone network to process the high and low frequency information of image features, so as to enhance the image edge features and improve the detection capability of multi-scale pedestrians. The experimental results show the following points. ① The improved CornerNet-Squeeze model can effectively improve the detection precision of underground pedestrian while maintaining the real-time performance of the original algorithm on the data sets of range image, infrared image and fusion image. ② The detection precision of the model trained by the fusion image dataset is higher than that of the models trained by the infrared image dataset or the depth image dataset. The result shows that the fusion image can give full play to the advantages of the depth image and the infrared image, and is helpful to improve the detection precision of the model. ③ In the six scenes of different degrees of occlusion and multi-scale pedestrian target, the model trained by the improved CornerNet-Squeeze has the lowest pedestrian misdetection rate. ④ Compared with YOLOv 4, the average accuracy of the improved CornerNet-Squeeze algorithm on the COCO2014 pedestrian dataset is improved by 1.1%, and the detection speed is improved by 6.7%. ⑤ The improved CornerNet-Squeeze can effectively detect the small target in the image. The detection capability of the small target is obviously improved.

     

/

返回文章
返回