针对目前基于深度学习的带式输送机煤量检测算法参数量大、难以部署于边缘计算设备及缺乏定量检测的问题,提出一种基于改进DeepLabv3+的带式输送机煤量检测方法。将轻量化网络MobileNetV2作为DeepLabv3+的骨干网络进行特征提取,在尽可...针对目前基于深度学习的带式输送机煤量检测算法参数量大、难以部署于边缘计算设备及缺乏定量检测的问题,提出一种基于改进DeepLabv3+的带式输送机煤量检测方法。将轻量化网络MobileNetV2作为DeepLabv3+的骨干网络进行特征提取,在尽可能保证分割精度的同时提高计算速度;针对煤流与输送带的方向性特征及输送带像素边缘呈现细长条状结构特征,采用条状空洞空间金字塔池化(SASPP)进行加强,并将SASPP模块、1×1卷积与残差结构进行融合,得到CA−SASPP,加强深层次的特征提取;结合CBAM注意力机制实现对特征图中关键信息的加权聚焦。实验结果表明,改进DeepLabv3+模型在平均分割精度仅下降0.36%的情况下,参数量减少了85.58%,推理速度提升至113帧/s,较原方法提高了12帧/s,在保持与原模型相当的分割精度的同时,实现了显著的轻量化效果。基于语义分割结果,通过计算煤量与输送带区域的面积占比,实现了煤量的定量检测,为多级带式输送机的智能调速提供了理论依据。将改进DeepLabv3+模型通过TensorRT加速并部署至Jetson Orin Nano边缘计算设备,实现了煤流图像的实时处理与分析,降低了云端服务器的计算负担,满足了工业现场对实时性和准确性的需求。展开更多
With the continuous development of artificial intelligence and computer vision technology,numerous deep learning-based lane line detection methods have emerged.DeepLabv3+,as a classic semantic segmentation model,has f...With the continuous development of artificial intelligence and computer vision technology,numerous deep learning-based lane line detection methods have emerged.DeepLabv3+,as a classic semantic segmentation model,has found widespread application in the field of lane line detection.However,the accuracy of lane line segmentation is often compromised by factors such as changes in lighting conditions,occlusions,and wear and tear on the lane lines.Additionally,DeepLabv3+suffers from high memory consumption and challenges in deployment on embedded platforms.To address these issues,this paper proposes a lane line detection method for complex road scenes based on DeepLabv3+and MobileNetV4(MNv4).First,the lightweight MNv4 is adopted as the backbone network,and the standard convolutions in ASPP are replaced with depthwise separable convolutions.Second,a polarization attention mechanism is introduced after the ASPP module to enhance the model’s generalization capability.Finally,the Simple Linear Iterative Clustering(SLIC)superpixel segmentation algorithmis employed to preserve lane line edge information.MNv4-DeepLabv3+was tested on the TuSimple and CULane datasets.On the TuSimple dataset,theMean Intersection over Union(MIoU)and Mean Pixel Accuracy(mPA)improved by 1.01%and 7.49%,respectively.On the CULane dataset,MIoU andmPA increased by 3.33%and 7.74%,respectively.Thenumber of parameters decreased from 54.84 to 3.19 M.Experimental results demonstrate that MNv4-DeepLabv3+significantly optimizes model parameter count and enhances segmentation accuracy.展开更多
文摘针对目前基于深度学习的带式输送机煤量检测算法参数量大、难以部署于边缘计算设备及缺乏定量检测的问题,提出一种基于改进DeepLabv3+的带式输送机煤量检测方法。将轻量化网络MobileNetV2作为DeepLabv3+的骨干网络进行特征提取,在尽可能保证分割精度的同时提高计算速度;针对煤流与输送带的方向性特征及输送带像素边缘呈现细长条状结构特征,采用条状空洞空间金字塔池化(SASPP)进行加强,并将SASPP模块、1×1卷积与残差结构进行融合,得到CA−SASPP,加强深层次的特征提取;结合CBAM注意力机制实现对特征图中关键信息的加权聚焦。实验结果表明,改进DeepLabv3+模型在平均分割精度仅下降0.36%的情况下,参数量减少了85.58%,推理速度提升至113帧/s,较原方法提高了12帧/s,在保持与原模型相当的分割精度的同时,实现了显著的轻量化效果。基于语义分割结果,通过计算煤量与输送带区域的面积占比,实现了煤量的定量检测,为多级带式输送机的智能调速提供了理论依据。将改进DeepLabv3+模型通过TensorRT加速并部署至Jetson Orin Nano边缘计算设备,实现了煤流图像的实时处理与分析,降低了云端服务器的计算负担,满足了工业现场对实时性和准确性的需求。
文摘With the continuous development of artificial intelligence and computer vision technology,numerous deep learning-based lane line detection methods have emerged.DeepLabv3+,as a classic semantic segmentation model,has found widespread application in the field of lane line detection.However,the accuracy of lane line segmentation is often compromised by factors such as changes in lighting conditions,occlusions,and wear and tear on the lane lines.Additionally,DeepLabv3+suffers from high memory consumption and challenges in deployment on embedded platforms.To address these issues,this paper proposes a lane line detection method for complex road scenes based on DeepLabv3+and MobileNetV4(MNv4).First,the lightweight MNv4 is adopted as the backbone network,and the standard convolutions in ASPP are replaced with depthwise separable convolutions.Second,a polarization attention mechanism is introduced after the ASPP module to enhance the model’s generalization capability.Finally,the Simple Linear Iterative Clustering(SLIC)superpixel segmentation algorithmis employed to preserve lane line edge information.MNv4-DeepLabv3+was tested on the TuSimple and CULane datasets.On the TuSimple dataset,theMean Intersection over Union(MIoU)and Mean Pixel Accuracy(mPA)improved by 1.01%and 7.49%,respectively.On the CULane dataset,MIoU andmPA increased by 3.33%and 7.74%,respectively.Thenumber of parameters decreased from 54.84 to 3.19 M.Experimental results demonstrate that MNv4-DeepLabv3+significantly optimizes model parameter count and enhances segmentation accuracy.
