计算机工程与应用 ›› 2022, Vol. 58 ›› Issue (15): 1-17.DOI: 10.3778/j.issn.1002-8331.2112-0176
张艳,张明路,吕晓玲,郭策,蒋志宏
出版日期:
2022-08-01
发布日期:
2022-08-01
ZHANG Yan, ZHANG Minglu, LYU Xiaoling, GUO Ce, JIANG Zhihong
Online:
2022-08-01
Published:
2022-08-01
摘要: 目标检测的主要目的是在图像中快速精准地识别定位出预定义类别的目标。而随着深度学习技术的不断发展,检测算法在相应行业大、中目标已达到了不错的成效。鉴于小目标在图像中尺寸较小、特征不全、与图像中背景差异大等特点,基于深度学习的小目标检测算法性能仍需要进一步提升和优化;小目标检测在无人驾驶、医疗诊断、无人机导航等多个领域都有着广泛的需求,因此研究有着很高的应用价值。在文献调研的基础上,先给出小目标检测定义,找到当前小目标检测的重难点;根据这些重难点从六个研究方向分析当前研究现状,并总结各算法优缺点;结合文献及发展现状对该领域未来的研究方向做出合理预测与展望,为后续研究提供一定基础参考。
张艳, 张明路, 吕晓玲, 郭策, 蒋志宏. 深度学习小目标检测算法研究综述[J]. 计算机工程与应用, 2022, 58(15): 1-17.
ZHANG Yan, ZHANG Minglu, LYU Xiaoling, GUO Ce, JIANG Zhihong. Review of Research on Small Target Detection Based on Deep Learning[J]. Computer Engineering and Applications, 2022, 58(15): 1-17.
[1] 胡俊,顾晶晶,王秋红.基于遥感图像的多模态小目标检测[J].图学学报,2022,43(2):197-204. HU J,GU J J,WANG Q H.Multimodal small target detection based on remote sensing image[J].Journal of Graphics,2022,43(2):197-204. [2] 陶磊,洪韬,钞旭.基于YOLOv3的无人机识别与定位追踪[J].工程科学学报,2020,42(4):463-468. TAO L,HONG T,CHAO X.Drone identification and location tracking based on YOLOv3[J].Chinese Journal of Engineering,2020,42(4):463-468. [3] 曹俊豪.基于深度学习的行人检测算法研究[D].北京:北京邮电大学,2019. CAO J H.Study on pedestrian detection algorithm based on deep learning[D].Beijing:Beijing University of Posts and Telecommunications,2019. [4] 孙一飞,武继刚,张欣鹏.面向眼底图像小目标检测的无监督学习方法[J].计算机工程与科学,2019,41(11):2000-2006. SUN Y F,WU J G,ZHANG X P.Unsupervised learning for small objects detection in retinal images[J].Computer Engineering and Science,2019,41(11):2000-2006. [5] 姜健涛.基于深度学习的人脸识别技术研究[D].哈尔滨:哈尔滨工业大学,2019. JIANG J T.Research on face recognition technology based on deep learning[D].Harbin:Harbin Institute of Technology,2019. [6] 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. [7] GIRSHICK R.Fast R-CNN[C]//Proceedings of the IEEE International Conference on Computer Vision,2015:1440-1448. [8] REN S,HE K,GIRSHICK R,et al.Faster R-CNN:Towards realtime object detection with region proposal networks[J].Advances in Neural Information Processing Systems,2015,28:91-99. [9] HE K,ZHANG X,REN S,et al.Spatial pyramid pooling in deep convolutional networks for visual recognition[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2015,37(9):1904-1916. [10] KIM K H,HONG S,ROH B,et al.Pvanet:deep but lightweight neural networks for real-time object detection[J]. arXiv:1608.08021,2016. [11] GIDARIS S,KOMODAKIS N.Object detection via a multi-region and semantic segmentation-aware CNN model[C]//Proceedings of the IEEE International Conference on Computer Vision,2015:1134-1142. [12] HE K,GKIOXARI G,DOLLáR P,et al.Mask R-CNN[C]//Proceedings of the IEEE International Conference on Computer Vision,2017:2961-2969. [13] 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. [14] REDMON J,FARHADI A.YOLO9000:better,faster,stronger[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2017:7263-7271. [15] REDMON J,FARHADI A.Yolov3:an incremental improvement[J].arXiv:1804.02767,2018. [16] BOCHKOVSKIY A,WANG C Y,LIAO H Y M.YOLOv4:optimal speed and accuracy of object detection[J].arXiv:2004.10934,2020. [17] LIU W,ANGUELOV D,ERHAN D,et al.SSD:single shot multibox detector[C]//European Conference on Computer Vision.Cham:Springer,2016:21-37. [18] FU C Y,LIU W,RANGA A,et al.DSSD:deconvolutional single shot detector[J].arXiv:1701.06659,2017. [19] SHEN Z,LIU Z,LI J,et al.DSOD:learning deeply supervised object detectors from scratch[C]//Proceedings of the IEEE International Conference on Computer Vision,2017:1919-1927. [20] JEONG J,PARK H,KWAK N.Enhancement of SSD by concatenating feature maps for object detection[J].arXiv:1705.09587,2017. [21] LI Z,ZHOU F.FSSD:feature fusion single shot multi box detector[J].arXiv:1712.00960,2017. [22] QI L,LI B,CHEN L,et al.Ship target detection algorithm based on improved faster R-CNN[J].Electronics,2019,8(9):959. [23] YIN G,YU M,WANG M,et al.Research on highway vehicle detection based on faster R-CNN and domain adaptation[J].Applied Intelligence,2022,52:3483-3498. [24] WU Q,FENG D,CAO C,et al.Improved mask R-CNN for aircraft detection in remote sensing images[J].Sensors,2021,21(8):2618. [25] HU J,SHI C J R,ZHANG J.Saliency-based YOLO for single target detection[J].Knowledge and Information Systems,2021,63(3):717-732. [26] WANG G,DING H,YANG Z,et al.TRC-YOLO:a real-time detection method for lightweight targets based on mobile devices[J].IET Computer Vision,2021,16(2):126-142. [27] GAI R,CHEN N,YUAN H.A detection algorithm for cherry fruits based on the improved YOLO-v4 model[J].Neural Computing and Applications,2021:1-12. [28] WANG X,HUA X,XIAO F,et al.Multi-object detection in traffic scenes based on improved SSD[J].Electronics,2018,7(11):302. [29] JIA D,ZHOU J,ZHANG C.Detection of cervical cells based on improved SSD network[J].Multimedia Tools and Applications,2021:1-17. [30] BAI D,SUN Y,TAO B,et al.Improved single shot multibox detector target detection method based on deep feature fusion[J].Concurrency and Computation:Practice and Experience,2021. [31] ZHOU Z,SHI Z,GUO Y,et al.Object detection in 20 years:a survey[J].arXiv:1905.05055,2019. [32] TONG K,WU Y,ZHOU F.Recent advances in small object detection based on deep learning:a review[J].Image and Vision Computing,2020,97:103910. [33] CHEN G,WANG H,CHEN K,et al.A survey of the four pillars for small object detection:multiscale representation,contextual information,super-resolution,and region proposal[J].IEEE Transactions on Systems,Man,and Cyber-Netics:Systems,2020,52(2):936-953. [34] LIU Y,SUN P,WERGELES N,et al.A survey and performance evaluation of deep learning methods for small object detection[J].Expert Systems with Applications,2021,172:114602. [35] 刘晓楠,王正平,贺云涛,等.基于深度学习的小目标检测研究综述[J].战术导弹技术,2019(1):100-107. LIU X N,WANG Z P,HE Y T,et al.Research on small target detection based on deep learning[J].Tactical Missile Technology,2019(1):100-107. [36] 刘洋,战荫伟.基于深度学习的小目标检测算法综述[J].计算机工程与应用,2021,57(2):37-48. LIU Y,ZHAN Y W.Survey of small object detection algorithms based on deep learning[J].Computer Engineering and Applications,2021,57(2):37-48. [37] 李红光,于若男,丁文锐.基于深度学习的小目标检测研究进展[J].航空学报,2021,42(7):107-125. LI H G,YU R N,DING W R.Research development of small object traching based on deep learning[J].Acta Aeronautica et Astronautica Sinica,2021,42(7):107-125. [38] 刘颖,刘红燕,范九伦,等.基于深度学习的小目标检测研究与应用综述[J].电子学报,2020,48(3):590-601. LIU Y,LIU H Y,FAN J L,et al.A survey of research and application of small object detection based on deep learning[J].Acta Electronica Sinica,2020,48(3):590-601. [39] 刘洪江,王懋,刘丽华,等.基于深度学习的小目标检测综述[J].计算机工程与科学,2021,43(8):1429-1442. LIU H J,WANG M,LIU L H,et al.A survery of small target detection based on deep learning[J].Computer Engineering and Science,2021,43(8):1429-1442. [40] 李科岑,王晓强,林浩,等.深度学习中的单阶段小目标检测方法综述[J].计算机科学与探索,2022,16(1):41-58. LI K C,WANG X Q,LIN H,et al.Survey of one-stage small object detection methods in deep learning[J].Journal of Frontiers of Computer Science and Technology,2022,16(1):41-58. [41] 高新波,莫梦竟成,汪海涛,等.小目标检测研究进展[J].数据采集与处理,2021,36(3):391-417. GAO X B,MO M J C,WANG H T,et al.Recent advances in small object detection[J].Journal Data Acquisition and Processing,2021,36(3):391-417. [42] INOUE H.Data augmentation by pairing samples for images classification[J].arXiv:1801.02929,2018. [43] ZHANG H,CISSE M,DAUPHIN Y N,et al.Mixup:beyond em pirical risk minimization[J].arXiv:1710. 09412,2017. [44] DEVRIES T,TAYLOR G W.Improved regularization of convolu tional neural networks with cutout[J].arXiv:1708.04552,2017. [45] YUN S,HAN D,OH S J,et al.Cutmix:regularization strategy to train strong classifiers with localizable features[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision,2019:6023-6032. [46] HARRIS E,MARCU A,PAINTER M,et al.Fmix:enhancing mixed sample data augmentation[J].arXiv:2002.12047,2020. [47] GOODFELLOW I J,POUGET-ABADIE J,MIRZA M,et al.Generative adversarial networks[C]//Advances in Neural Information Processing Systems,2014:2672-2680. [48] CUBUK E D,ZOPH B,MANE D,et al.Autoaugment:learning augmentation policies from data[J].arXiv:1805. 09501,2018. [49] CUBUK E D,ZOPH B,SHLENS J,et al.Randaugment:practical automated data augmentation with a reduced search space[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops,2020:702-703. [50] LIM S,KIM I,KIM T,et al.Fast autoaugment[C]//Advances in Neural Information Processing Systems,2019:6665-6675. [51] HO D,LIANG E,CHEN X,et al.Population based augmentation:efficient learning of augmentation policy schedules[C]//International Conference on Machine Learning,2019:2731-2741. [52] MüLLER S G,HUTTER F.TrivialAugment:tuning-free yet state-of-the-art data augmentation[J].arXiv:2103. 10158,2021. [53] KISANTAL M,WOJNA Z,MURAWSKI J,et al.Aug mentation for small object detection[J].arXiv:1902. 07296,2019. [54] CHEN C,ZHANG Y,LV Q,et al.RRNet:a hybrid detector for object detection in drone-captured images[C]//Proceedings of the IEEE International Conference on Computer Vision Workshops,2019. [55] CHEN Y,ZHANG P,LI Z,et al.Stitcher:feedback-driven data provider for object detection[J].arXiv:2004.12432,2020. [56] ZOPH B,CUBUK E D,GHIASI G,et al.Learning data augmentation strategies for object detection[J].arXiv:1906.11172,2019. [57] LIN T Y,DOLLáR P,GIRSHICK R,et al.Feature pyramid networks for object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2017:2117-2125. [58] GUO C,FAN B,ZHANG Q,et al.Augfpn:improving multi-scale feature learning for object detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition,2020:12595-12604. [59] GHIASI G,LIN T Y,LE Q V.Nas-fpn:learning scalable feature pyramid architecture for object detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition,2019:7036-7045. [60] LUO Y,CAO X,ZHANG J,et al.CE-FPN:enhancing channel information for object detection[J].arXiv:2103. 10643,2021. [61] KIM S W,KOOK H K,SUN J Y,et al.Parallel feature pyramid network for object detection[C]//Proceedings of the European Conference on Computer Vision(ECCV),2018:234-250. [62] ZHAO B J,ZHAO B Y,TANG L B,et al.Multi-scale object detection by top-down and bottom-up feature pyramid network[J].Journal of Systems Engineering and Electronics,2019,30(1):1-12. [63] LI Z,ZHOU F.FSSD:feature fusion single shot multibox detector[J].arXiv:1712.00960,2017. [64] FU C Y,LIU W,RANGA A,et al.DSSD:deconvolutional single shot detector[J].arXiv:1701.06659,2017. [65] 李文涛,彭力.多尺度通道注意力融合网络的小目标检测算法[J].计算机科学与探索,2021,15(12):2390-2400. LI W T,PENG L.Small objects detection algorithm with multi-scale channel attention fusion network[J].Journal of Frontiers of Computer Science and Technology,2021,15(12):2390-2400. [66] 陈欣,万敏杰,马超,等.采用多尺度特征融合SSD的遥感图像小目标检测[J].光学精密工程,2021,29(11):2672-2682. CHEN X,WAN M J,MA C,et al.Recognition of small targets in remote sensing image using multi-scale feature fusion-based shot multi-box detector[J].Optics and Precision Engineering,2021,29(11):2672-2682. [67] 李晖晖,周康鹏,韩太初.基于CReLU和FPN改进的SSD舰船目标检测[J].仪器仪表学报,2020,41(4):183-190. LI H H,ZHOU K P,HAN T C.Ship object detection based on SSD improved with CReLU and FPN[J].Chinese Journal of Scientific Instrument,2020,41(4):183-190. [68] 赵彤,刘洁瑜,沈强.一种改进的多门控特征金字塔网络[J].光学学报,2019,39(8):235-244. ZHAO T,LIU J Y,SHEN Q.An improved multi-gated feature pyramid network[J].Acta Optica Sinica,2019,39(8):235-244. [69] 李宝奇,贺昱曜,强伟,等.基于并行附加特征提取网络的SSD地面小目标检测模型[J].电子学报,2020,48(1):84-91. LI B Q,HE Y Y,QIANG W,et al.SSD with parallel additional feature extraction network for ground small target detection[J].Acta Electronica Sinica,2020,48(1):84-91. [70] 梁延禹,李金宝.多尺度非局部注意力网络的小目标检测算法[J].计算机科学与探索,2020,14(10):1744-1753. LIANG Y Y,LI J B.Small objects detection method based on multi-scale non-local attention network[J].Journal of Frontiers of Computer Science and Technology,2020,14(10):1744-1753. [71] MENG J,JIANG P,WANG J,et al.A mobileNet-SSD model with FPN for waste detection[J].Journal of Electrical Engineering & Technology,2021:1-7. [72] QU J,SU C,ZHANG Z,et al.Dilated convolution and feature fusion SSD network for small object detection in remote sensing images[J].IEEE Access,2020,8:82832-82843. [73] REN K,HUANG L,FAN C,et al.Real-time traffic sign detection network using DS-DetNet and lite fusion FPN[J].Journal of Real-Time Image Processing,2021,18:2181-2191. [74] KONG T,SUN F,TAN C,et al.Deep feature pyramid recon-figuration for object detection[C]//Proceedings of the European Conference on Computer Vision(ECCV),2018:169-185. [75] ZHANG Z,QIAO S,XIE C,et al.Single-shot object detection with enriched semantics[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2018:5813-5821. [76] XUE Z,CHEN W,LI J.Enhancement and fusion of multi-scale feature maps for small object detection[C]//2020 39th Chinese Control Conference(CCC),2020:7212-7217. [77] NAYAN A A,SAHA J,MOZUMDER A N,et al.Real time detection of small objects[J].International Journal of Innovative Technology and Exploring Engineering,2020,9(5):837. [78] DENG C,WANG M,LIU L,et al.Extended feature pyramid network for small object detection[J].arXiv:2003. 07021,2020. [79] QI G,ZHANG Y,WANG K,et al.Small object detection method based on adaptive spatial parallel convolution and fast multi-scale fusion[J].Remote Sensing,2022,14(2):420. [80] ZHU C,TAO R,LUU K,et al.Seeing small faces from robust anchor’s perspective[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2018:5127-5136. [81] WANG J,CHEN K,YANG S,et al.Region proposal by guided anchoring[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2019:2965-2974. [82] LI Y,YU Y,LI Z,et al.Pixel-anchor:a fast oriented scene text detector with combined networks[J].arXiv:1811.07432,2018. [83] 王毓玮,史国友,林佳木.基于改进Faster R-CNN的SAR舰船图像检测[J].船舶工程,2021,43(8):29-33. WANG Y W,SHI G Y,LIN J M.SAR ship image detection based on improved Faster R-CNN[J].Ship Engineering,2021,43(8):29-33. [84] 周慧,严凤龙,褚娜,等.一种改进复杂场景下小目标检测模型的方法[J].计算机工程与应用,2022,58(11):187-192. ZHOU H,YAN F L,CHU N,et al.Approach to improve detection model for small object in complex scenes[J].Computer Engineering and Applications,2022,58(11):187-192. [85] LAW H,DENG J.Cornernet:detecting objects as paired keypoints[C]//Proceedings of the European Conference on Computer Vision(ECCV),2018:734-750. [86] DUAN K,BAI S,XIE L,et al.Centernet:keypoint triplets for object detection[C]//Proceedings of the IEEE International Conference on Computer Vision,2019:6569-6578. [87] DONG Z,LI G,LIAO Y,et al.CentripetalNet:pursuing high-quality keypoint pairs for object detection[J].arXiv:2003.09119,2020. [88] ZHANG S,CHI C,YAO Y,et al.Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR),2020. [89] CAI Z,VASCONCELOS N.Cascade R-CNN:delving into high quality object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2018:6154-6162. [90] 汪跃东.基于Cascade-RCNN的密集场景行人检测算法研究[D].重庆:重庆理工大学,2021. WANG Y D.Pedestrian detection algorithm in dense scenes based on Cascade-RCNN[D].Chongqing:Chongqing University of Technology,2021. [91] HAN X.Modified cascade RCNN based on contextual information for vehicle detection[J].Sensing and Imaging,2021,22(1):1-19. [92] SHI X,LI Z,YU H.Adaptive threshold cascade faster RCNN for domain adaptive object detection[J].Multimedia Tools and Applications,2021,80:25291-25308. [93] 刘艳萍,刘甜.改进的Cascade RCNN行人检测算法研究[J].计算机工程与应用,2022,58(4):229-236. LIU Y P,LIU T.Improved Cascade RCNN pedestrian detection algorithm research[J].Computer Engineering and Applications,2022,58(4):229-236. [94] 李松江,吴宁,王鹏,等.基于改进Cascade RCNN的车辆目标检测方法[J].计算机工程与应用,2021,57(5):123-130. LI S J,WU N,WANG P,et al.Vehicle target detection method based on improved Cascade RCNN[J].Computer Engineering and Applications,2021,57(5):123-130. [95] ZHANG W,LI Q,WU Q M,et al.A novel ship target detection algorithm based on error self-adjustment extreme learning machine and cascade classifier[J].Cognitive Computation,2019,11(1):110-124. [96] HE K,ZHANG X,REN S.Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2016:770-778. [97] RASHWAN A,AGARWAL R,KALRA A,et al.MatrixNets:a new scale and aspect ratio aware architecture for object detection[J].arXiv:2001.03194,2020. [98] LIANG B,WU S,XU K,et al.Butterfly detection and clas-sification based on integrated YOLO algorithm[C]//International Conference on Genetic and Evolutionary Computing.Singapore:Springer,2019:500-512. [99] 高晔,郭松宜,厍向阳.基于残差收缩网络的遥感图像目标检测算法[J/OL].计算机工程与应用:1-9[2021-12-04].http://kns.cnki.net/kcms/detail/11.2127.TP.20211116.2043. 014.html. GAO Y,GUO S Y,SHE X Y.Remote sensing image target detection algorithm based on residual shrinkage network[J/OL].Computer Engineering and Applications:1-9[2021-12-04].http://kns.cnki.net/kcms/detail/11.2127.TP. 20211116.2043.014.html. [100] YUAN P,LIN S,CUI C,et al.HS-ResNet:hierarchical-split block on convolutional neural network[J].arXiv:2010.07621,2020. [101] BAI Y,ZHANG Y,DING M.SOD-MTGAN:small object detection via multi-task generative adversarial network[C]//Proceedings of the European Conference on Computer Vision(ECCV),2018:206-221. [102] WANG H,WANG J,BAI K,et al.Centered multi-task generative adversarial network for small object detection[J].Sensors,2021,21(15):5194. [103] COURTRAI L,PHAM M T,LEFèVRE S.Small object detection in remote sensing images based on super-resolution with auxiliary generative adversarial networks[J].Remote Sensing,2020,12(19):3152. [104] ZHAO B,WANG C,FU Q,et al.A novel pattern for infrared small target detection with generative adversarial network[J].IEEE Transactions on Geoscience and Remote Sensing,2020,59(5):4481-4492. [105] LI J,LIANG X,WEI Y,et al.Perceptual generative adversarial networks for small object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2017:1222-1230. [106] RABBI J,RAY N,SCHUBERT M,et al.Small-object detection in remote sensing images with end-to-end edge-enhanced GAN and object detector network[J].Remote Sensing,2020,12(9):1432. [107] JIANG K,WANG Z,YI P,et al.Edge-enhanced GAN for remote sensing image superresolution[J].IEEE Transactions on Geoscience and Remote Sensing,2019,57(8):5799-5812. [108] WANG X,YU K,WU S,et al.Esrgan:enhanced superresolution generative adversarial networks[C]//Proceedings of the European Conference on Computer Vision(ECCV) Workshops,2018. [109] YU B,TAO D.Anchor cascade for efficient face detection[J].IEEE Transactions on Image Processing,2018,28(5):2490-2501. [110] ZHU Y,ZHAO C,WANG J,et al.Couplenet:coupling global structure with local parts for object detection[C]//Proceedings of the IEEE International Conference on Computer Vision,2017:4126-4134. [111] ZHU Y,ZHAO C,GUO H,et al.Attention couplenet:fully convolutional attention coupling network for object detection[J].IEEE Transactions on Image Processing,2018,28(1):113-126. [112] QIAO S,CHEN L C,YUILLE A.DetectoRS:detecting objects with recursive feature pyramid and switchable atrous convolution[J].arXiv:2006.02334,2020. [113] 孔慧芳,冯超,胡杰.基于特征上下文编码的实时语义分割网络[J].合肥工业大学学报(自然科学版),2021,44(12):1621-1626. KONG H F,FENG C,HU J.Real-time semantic segmentation network based on feature context encoding[J].Journal of Hefei University of Technology(Natural Science),2021,44(12):1621-1626. [114] 张馨月,降爱莲.融合特征增强和自注意力的SSD小目标检测算法[J].计算机工程与应用,2022,58(5):247-255. ZHANG X Y,JIANG A L.SSD small target detection algorithm combining feature enhancement and self-attention[J].Computer Engineering and Applications,2022,58(5):247-255. [115] YU C,WANG J,GAO C,et al.Context prior for scene segmentation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition,2020:12416-12425. [116] LIM J S,ASTRID M,YOON H J,et al.Small object detection using context and attention[C]//2021 International Conference on Artificial Intelligence in Information and Communication(ICAIIC),2021:181-186. [117] RAMACHANDRAN P,ZOPH B,LE Q V.Searching for activation functions[J].arXiv:1710.05941,2017. [118] LIN G,SHEN W.Research on convolutional neural network based on improved Relu piecewise activation function[J].Procedia Computer Science,2018,131:977-984. [119] 徐浩,杨德刚,蒋倩倩,等.基于SSD的轻量级车辆检测网络改进[J].计算机工程与应用,2022,58(12):209-217. XU H,YANG D G,JIANG Q Q,et al.Improvement of light weight vehicle detection network based on SSD[J].Computer Engineering and Applications,2022,58(12):209-217. [120] 周非,李阳,范馨月.图像分类卷积神经网络的反馈损失计算方法改进[J].小型微型计算机系统,2019,40(7):1532-1537. ZHOU F,LI Y,FAN X Y.Improved loss calculation algrithm for convolutional neural networks in image classification application[J].Journal of Chinese Computer Systems,2019,40(7):1532-1537. [121] HU X,XU X,XIAO Y,et al.SINet:a scale-insensitive convolutional neural network for fast vehicle detection[J].IEEE Transactions on Intelligent Transportation Systems,2018,20(3):1010-1019. [122] 刘淼,王晶,董桂官,等.基于改进池化层的弱标记声音事件检测[J].信号处理,2021,37(10):1907-1913. LIU M,WANG J,DONG G G,et al.Weakly labeled sound event detection based on improved pooling layer[J].Journal of Signal Processing,2021,37(10):1907-1913. [123] LU Y,JAVIDI T,LAZEBNIK S.Adaptive object detection using adjacency and zoom prediction[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2016:2351-2359. [124] 刘安旭,黎向锋,刘晋川,等.改进卷积空间传播网络的单目图像深度估计[J].电子测量技术,2021,44(23):78-85. LIU A X,LI X F,LIU J C,et al.Monocular image depth estimation of improved convolutional spatial propagation network[J].Electronic Measurement Technology,2021,44(23):78-85. [125] IANDOLA F N,HAN S,MOSKEWICZ M W,et al.SqueezeNet:AlexNet-level accuracy with 50x fewer parameters and<0.5?MB model size[J].arXiv:1602. 07360,2016. [126] HOWARD A G,ZHU M,CHEN B,et al.Mobilenets:efficient convolutional neural networks for mobile vision applications[J].arXiv:1704.04861,2017. [127] SANDLER M,HOWARD A,ZHU M,et al.Mobilenetv2:inverted residuals and linear bottlenecks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2018:4510-4520. [128] HOWARD A,SANDLER M,CHU G,et al.Searching for mobilenetv3[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision,2019:1314-1324. [129] ZHANG X,ZHOU X,LIN M,et al.Shufflenet:an extremely efficient convolutional neural network for mobile devices[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2018:6848-6856. [130] MA N,ZHANG X,ZHENG H T,et al.Shufflenet v2:practical guidelines for efficient CNN architecture design[C]//Proceedings of the European Conference on Computer Vision(ECCV),2018:116-131. [131] ZHANG Y,BI S,DONG M,et al.The implementation of CNN-based object detector on ARM embedded platforms[C]//2018 IEEE 16th International Conference on Dependable,Autonomic and Secure Computing,16th International Conference on Pervasive Intelligence and Computing,4th International Conference on Big Data Intelligence and Computing and Cyber Science and Tech-nology Congress(DASC/PiCom/DataCom/CyberSciTech),2018:379-382. [132] WONG A,FAMUORI M,SHAFIEE M J,et al.Yolo nano:a highly compact you only look once convolutional neural network for object detection[C]//2019 Fifth Workshop on Energy Efficient Machine Learning and Cognitive Computing-NeurIPS Edition(EMC2-NIPS),2019:22-25. [133] CHOI J,CHUN D,KIM H,et al.Gaussian yolov3:an accurate and fast object detector using localization uncertainty for autonomous driving[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision,2019:502-511. [134] QIN Z,LI Z,ZHANG Z,et al.ThunderNet:towards real-time generic object detection on mobile devices[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision,2019:6718-6727. [135] 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. [136] CHEN C,LIU M,MENG X,et al.Refinedetlite:a lightweight one-stage object detection framework for cpu-only devices[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops,2020:700-701. [137] XIONG Y,LIU H,GUPTA S,et al.Mobiledets:searching for object detection architectures for mobile accelerators[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition,2021:3825-3834. [138] HOWARD A G,ZHU M,CHEN B,et al.Mobilenets:efficient convolutional neural networks for mobile vision applications[J].arXiv:1704.04861,2017. [139] ZHANG X,ZHOU X,LIN M,et al.Shufflenet:an extremely efficient convolutional neural network for mobile devices[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2018:6848-6856. [140] XIONG Y,LIU H,GUPTA S,et al.MobileDets:searching for object detection architectures for mobile accelerators[J].arXiv:2004.14525,2020. [141] 景丽婷.基于多尺度超分辨率的小目标检测算法研究[D].厦门:厦门大学,2019. JING L T.Research on small object detection algorithm based on multi-scale super-resolution[D].Xiamen:Xiamen University,2019. [142] NOH J,BAE W,LEE W,et al.Better to follow,follow to be better:towards precise supervision of feature super-resolution for small object detection[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision,2019:9725-9734. [143] LIU Z,GAO G,SUN L,et al.HRDNet:high-resolution detection network for small objects[C]//2021 IEEE International Conference on Multimedia and Expo(ICME),2021:1-6. [144] 吴湘宁,贺鹏,邓中港,等.一种基于注意力机制的小目标检测深度学习模型[J].计算机工程与科学,2021,43(1):95-104. WU X N,HE P,DENG Z G,et al.A deep learning model for small object detection based on attention mechanism[J].Computer Engineering and Science,2021,43(1):95-104. [145] ZHANG Y,CHEN Y,HUANG C,et al.Object detection network based on feature fusion and attention mechanism[J].Future Internet,2019,11(1):9. [146] LV Z,WANG W,XU Z,et al.Fine-grained object detection method using attention mechanism and its application in coal-gangue detection[J].Applied Soft Computing,2021,113:107891. |
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