Optics and Precision Engineering, Volume. 30, Issue 12, 1478(2022)
Vehicle detection based on FVOIRGAN-Detection
[1] BEHL A, JAFARI O H, MUSTIKOVELA S K et al. Bounding boxes, segmentations and object coordinates: how important is recognition for 3D scene flow estimation in autonomous driving scenarios?[C], 2593-2602(2017).
[2] CHEN Y P, WANG J K, LI J et al. LiDAR-video driving dataset: learning driving policies effectively[C], 5870-5878(2018).
[3] GEIGER A, LENZ P, URTASUN R. Are we ready for autonomous driving? The KITTI vision benchmark suite[J]. 2012 IEEE Conference on Computer Vision and Pattern Recognition, 3354-3361(2012).
[4] KWON S K, HYUN E, LEE J H et al. Detection scheme for a partially occluded pedestrian based on occluded depth in lidar-radar sensor fusion[J]. Optical Engineering, 56, 113112(2017).
[5] ZHANG S, ZHAO X, LEI W B et al. Front vehicle detection based on multi-sensor fusion for autonomous vehicle[J]. Journal of Intelligent & Fuzzy Systems, 38, 365-377(2020).
[6] [6] 6王中宇, 倪显扬, 尚振东. 利用卷积神经网络的自动驾驶场景语义分割[J]. 光学 精密工程, 2019, 27(11): 2429-2438. doi: 10.3788/ope.20192711.2429WANGZ Y, NIX Y, SHANGZ D. Autonomous driving semantic segmentation with convolution neural networks[J]. Opt. Precision Eng., 2019, 27(11): 2429-2438.(in Chinese). doi: 10.3788/ope.20192711.2429
[7] [7] 7杨军, 党吉圣. 采用深度级联卷积神经网络的三维点云识别与分割[J]. 光学 精密工程, 2020, 28(5): 1187-1199. doi: 10.3788/OPE.20202805.1187YANGJ, DANGJ S. Recognition and segmentation of three-dimensional point cloud based on deep cascade convolutional neural network[J]. Opt. Precision Eng., 2020, 28(5): 1187-1199.(in Chinese). doi: 10.3788/OPE.20202805.1187
[8] CALTAGIRONE L, BELLONE M, SVENSSON L et al. LIDAR-camera fusion for road detection using fully convolutional neural networks[J]. Robotics and Autonomous Systems, 111, 125-131(2019).
[9] ASVADI A, GARROTE L, PREMEBIDA C et al. Multimodal vehicle detection: fusing 3D-LIDAR and color camera data[J]. Pattern Recognition Letters, 115, 20-29(2018).
[10] CHEN X Z, MA H M, WAN J et al. Multi-view 3D object detection network for autonomous driving[C], 6526-6534(2017).
[11] LIANG M, YANG B, CHEN Y et al. Multi-task multi-sensor fusion for 3D object detection[C], 7337-7345(2019).
[12] FANG L J, ZHAO X, ZHANG S Q. Small-objectness sensitive detection based on shifted single shot detector[J]. Multimedia Tools and Applications, 78, 13227-13245(2019).
[13] KU J, MOZIFIAN M, LEE J et al. Joint 3D proposal generation and object detection from view aggregation[C], 1-8(2018).
[14] LIANG M, YANG B, WANG S et al. Deep continuous fusion for multi-sensor 3d object detection[C], 641-656(2018).
[15] [15] 15陈俊英, 白童垚, 赵亮. 互注意力融合图像和点云数据的3D目标检测[J]. 光学 精密工程, 2021, 29(9): 2247-2254. doi: 10.37188/OPE.20212909.2247CHENJ Y, BAIT Y, ZHAOL. 3D object detection based on fusion of point cloud and image by mutual attention[J]. Opt. Precision Eng., 2021, 29(9): 2247-2254.(in Chinese). doi: 10.37188/OPE.20212909.2247
[16] SCHLOSSER J, CHOW C K, KIRA Z. Fusing LIDAR and images for pedestrian detection using convolutional neural networks[C], 2198-2205(2016).
[17] GUPTA S, GIRSHICK R, ARBELÁEZ P et al. Learning rich features from RGB-D images for object detection and segmentation[C], 345-360(2014).
[18] JOLICOEUR-MARTINEAU A. The relativistic discriminator: a key element missing from standard GAN[J]. arXiv preprint arXiv.
[19] GEIRHOSET R, RUBISCH P et al. ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness[J]. arXiv preprint arXiv.
[20] RADFORD A, METZ L, CHINTALA S. Unsupervised representation learning with deep convolutional generative adversarial networks[J]. arXiv preprint.
[21] MA J Y, YU W, LIANG P W et al. FusionGAN: a generative adversarial network for infrared and visible image fusion[J]. Information Fusion, 48, 11-26(2019).
[22] FAWCETT T. An introduction to ROC analysis[J]. Pattern Recognition Letters, 27, 861-874(2006).
[23] GEIGER A, LENZ P, STILLER C et al. Vision meets robotics: the KITTI dataset[J]. The International Journal of Robotics Research, 32, 1231-1237(2013).
[24] CHEN X Z, KUNDU K, ZHANG Z Y et al. Monocular 3D object detection for autonomous driving[C], 2147-2156(2016).
[25] CHEN X, KUNDU K, ZHU Y et al. 3d object proposals for accurate object class detection[J]. Advances in Neural Information Processing Systems, 424-432(2015).
[26] BRAZIL G, LIU X M. M3D-RPN: monocular 3D region proposal network for object detection[C], 9286-9295(2019).
Get Citation
Copy Citation Text
Hao ZHANG, Jianhua YANG, Haiyang HUA. Vehicle detection based on FVOIRGAN-Detection[J]. Optics and Precision Engineering, 2022, 30(12): 1478
Category: Information Sciences
Received: Dec. 14, 2021
Accepted: --
Published Online: Jul. 5, 2022
The Author Email: Haiyang HUA (c3i11@sia.cn)