计算机科学 ›› 2024, Vol. 51 ›› Issue (7): 206-213.doi: 10.11896/jsjkx.230400086
娄铮铮, 张欣, 胡世哲, 吴云鹏
LOU Zhengzheng, ZHANG Xin, HU Shizhe, WU Yunpeng
摘要： 文中提出了一个基于深度可分离卷积和注意力机制的雾天目标检测模型,旨在实现在雾天场景中对目标的快速、准确检测。该模型由去雾模块和检测模块组成,并在训练过程中共同训练。为确保模型在雾天场景中检测的准确性和实时性,在去雾模块方面,采用AODNet对输入图像进行去雾处理,以降低雾对图像中待检测目标的干扰,在检测模块中使用改进后的YOLOX_s模型,输出目标的分类置信度和位置坐标。为提升网络的检测性能,在YOLOX_s基础上采用深度可分离卷积和注意力机制来提高特征提取能力,扩大特征图感受野。所提模型能提高有雾场景中模型的检测精度,且不增加模型参数量和计算量。实验结果表明,所提模型在RTTS数据集和合成有雾目标检测数据集上均表现出色,有效提高了模型在雾天场景中的检测精度。与基准模型相比,平均精度(mAP@50_95)分别提升了1.9%和2.37%。
中图分类号:
| [1]GIRSHICK R,DONAHUE J,DARRELL T,et al.Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2014:580-587. [2]REDMON J,DIVVALA S,GIRSHICK R,et al.You only look once:Unified,real-time object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:779-788. [3]LIU W,ANGUELOV D,ERHAN D,et al.Ssd:Single shotmultibox detector[C]//Computer Vision+ECCV 2016:14th European Conference,Amsterdam,The Netherlands,Part I 14.Springer International Publishing,2016:21-37. [4]GE Z,LIU S,WANG F,et al.Yolox:Exceeding yolo series in 2021[J].arXiv:2107.08430,2021. [5]LI B,PENG X,WANG Z,et al.Aod-net:All-in-one dehazing network[C]//Proceedings of the IEEE International Conference on Computer Vision.2017:4770-4778. [6]REN S,HE K,GIRSHICK R,et al.Faster R-CNN:Towards Real-Time Object Detection with Region Proposal Networks[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2017,39(6):1137-1149. [7]HUANG S C,LE T H,JAW D W.DSNet:Joint semantic lear-ning for object detection in inclement weather conditions[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2020,43(8):2623-2633. [8]REDMON J,FARHADIA.YOLO9000:better,faster,stronger[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:7263-7271. [9]REDMON J,FARHADI A.Yolov3:An incremental improve-ment[J].arXiv:1804.02767,2018. [10]HE K,ZHANG X,REN S,et al.Deep residual learning forimage recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:770-778. [11]BOCHKOVSKIY A,WANG C Y,LIAO H Y M.Yolov4:Optimal speed and accuracy of object detection[J].arXiv:2004.10934,2020. [12]WANG C Y,LIAO H Y M,WU Y H,et al.CSPNet:A new backbone that can enhance learning capability of CNN[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops.2020:390-391. [13]LI C,LI L,JIANG H,et al.YOLOv6:A single-stage object detection framework for industrial applications[J].arXiv:2209.02976,2022. [14]WANG C Y,BOCHKOVSKIY A,LIAO H Y M.YOLOv7:Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[C]//Proceedings of the IEEE/CVF Confe-rence on Computer Vision and Pattern Recognition.2023:7464-7475. [15]XIE Y H,XIE Y,CHEN Y.Object Detection in Real MistyScenes [J].Journal of Computer Aided Design and Graphics,2021,33(5):733-745. [16]HNEWA M,RADHA H.Multiscale domain adaptive yolo for cross-domain object detection[C]//2021 IEEE International Conference on Image Processing(ICIP).IEEE,2021:3323-3327. [17]LIU W,REN G,YU R,et al.Image-adaptive YOLO for object detection in adverse weather conditions[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2022:1792-1800. [18]LI X X,QIANG J,LIU W J,et al.Research on Traffic Object Detection Method in Fog Based on Dual Backbone Network[J].Journal of Chongqing Technology and Business University(Natural Science Edition),2023,40(4):25-34. [19]XU S,WANG X,LV W,et al.PP-YOLOE:An evolved version of YOLO[J].arXiv:2203.16250,2022. [20]WU Y,HE K.Group normalization[C]//Proceedings of theEuropean Conference on Computer Vision(ECCV).2018:3-19. [21]LI B,REN W,FU D,et al.Benchmarking single-image dehazing and beyond[J].IEEE Transactions on Image Processing,2018,28(1):492-505. [22]EVERINGHAM M,VAN GOOL L,WILLIAMS C K I,et al.The pascal visual object classes(voc)challenge[J].International Journal of Computer Vision,2010,88:303-338. |
|
||
首页