摘要
行人检测是实现矿用车辆无人化的一项关键技术,煤矿井下弱光环境中捕获的图像可见度不佳,极大地影响了行人检测效果。现有行人检测方法忽略了井下弱光环境对目标检测精度的影响,检测效果不理想。针对该问题,提出一种基于Dense-YOLO网络的井下行人检测模型。将弱光图像分解为光照图和反射图,采用Gamma变换、加权对数变换、限制对比度的自适应直方图均衡(CLAHE)对光照图进行增强处理,采用亮度权值和色彩权值对增强后的图像进行加权融合;采用双边滤波算法对反射图进行处理,以增强图像纹理;将增强后的光照图和经过双边滤波处理的反射图逐点相乘,重构出RGB图,并采用ROF去噪模型对融合后的图像进行全局去噪,得到最终的增强图像。将含有残差块的Dense模块添加到YOLOv3中,构建基于Dense-YOLO网络的井下行人检测模型,残差块的加入有利于避免在网络训练过程中出现梯度消失和梯度爆炸等问题。实验结果表明:对弱光图像进行增强处理能够有效提高图像可见度和行人检测效果;Dense-YOLO网络对增强图像的漏检率为4.55%,相较于RetinaNet网络降低了14.91%,基于Dense-YOLO网络的井下行人检测模型有效降低了行人检测漏检率。
The pedestrian detection is a key technology to realize unmanned mining vehicles. The visibility of images captured in low light environment in coal mine is poor, which greatly affects the effect of pedestrian detection. The existing pedestrian detection methods ignore the influence of underground low light environment on target detection precision, and the detection effect is not ideal. In order to solve this problem, an underground pedestrian detection model based on Dense-YOLO network is proposed. The low light images are decomposed into light image and reflection image, and the light image is enhanced by Gamma transformation, weighted logarithmic transformation and contrast-limited adaptive histogram equalization(CLAHE). The enhanced images are weighted and fused by brightness weight and color weight. The bilateral filtering algorithm is used to process the reflection image to enhance the texture of the image. The enhanced light image and the reflection image processed by bilateral filtering are multiplied point by point to reconstruct the RGB image, and the ROF denoising model is used to denoise the fused image globally to obtain the final enhanced image. The dense module with residual block is added to YOLOv3 to build underground pedestrian detection model based on Dense-YOLO network. The addition of residual block is beneficial to avoid gradient disappearance and gradient explosion in the network training process. The experimental results show that the image visibility and pedestrian detection can be improved effectively by enhancing the low light image. The missed detection rate of Dense-YOLO network for enhanced images is 4.55%, which is 14.91% lower than that of Retina Net network. The underground pedestrian detection model based on Dense-YOLO network effectively reduces the missed detection rate of pedestrian detection.
作者
张明臻
ZHANG Mingzhen(Department of Electronic,Electrical and Systems Engineering,University of Birmingham,Birmingham B152TT,UK)
出处
《工矿自动化》
北大核心
2022年第3期86-90,共5页
Journal Of Mine Automation
